Effect size calculation for meta-analysis

1 INTRODUCTION

This script organizes the meta-analysis data to calculate the effect size fishing has on different variables (log response ratio). We first look at all the data which was extracted from literature, and visualize the range of response ratios found there. Then, we select the rows of data we can use for meta-regression and calculating more advanced summary statistics. We have to do this since not all means have a standard deviation / other error measure that can be converted. These rows, if the SD cannot be imputed (see add_sd.rmd), have to be filtered out.

2 DATA WRANGLING

–> Load dataset with added sd’s where possible.

df <- get(load("../dataset/dataset.rdata"))

2.1 Organize columns to calculate lnRR

Response ratios can be calculated for all rows, but then there are 2 sets of data to work with:

–> ALL ROWS: Full data which we use only for checking range of response ratios. –> SELECTED ROWS: Selected data with only complete information rows.

• BA - Studies –> response1 = before , response 2 = after.
• CI - Studies –> response1 = control, response 2 = impact.
• BACI - Studies –> response1 = ratio after/before control, response 2 = ratio after/before impact.

# Response ratios and number of samples.
### Before-After
df$response1[df$Study.type == "Before After"] = df$Mean_Before_impact[df$Study.type == "Before After"]
df$n1[df$Study.type == "Before After"]        = df$n_Before_impact[df$Study.type == "Before After"]

### Control-Impact
df$response2[df$Study.type == "Before After"] = df$Mean_After_impact[df$Study.type == "Before After"]
df$n2[df$Study.type == "Before After"]        = df$n_After_impact[df$Study.type == "Before After"]

### BACI
df$response1[df$Study.type == "Control Impact"] = df$Mean_After_control[df$Study.type == "Control Impact"]
df$response2[df$Study.type == "Control Impact"] = df$Mean_After_impact[df$Study.type == "Control Impact"]
df$n1[df$Study.type == "Control Impact"]        = df$n_After_control[df$Study.type == "Control Impact"]
df$n2[df$Study.type == "Control Impact"]        = df$n_After_impact[df$Study.type == "Control Impact"]

df$AftCtrl_BefCtrl = df$Mean_After_control/df$Mean_Before_control
df$AftImpt_BefImpt = df$Mean_After_impact/df$Mean_Before_impact
df$response1[df$Study.type == "Before After Control Impact"] = df$AftCtrl_BefCtrl[df$Study.type == "Before After Control Impact"]
df$response2[df$Study.type == "Before After Control Impact"] = df$AftImpt_BefImpt[df$Study.type == "Before After Control Impact"]

df$n1[df$Study.type == "Before After Control Impact"]        = (df$n_After_control[df$Study.type == "Before After Control Impact"] 
                                                           + df$n_Before_control[df$Study.type == "Before After Control Impact"])/2
df$n2[df$Study.type == "Before After Control Impact"]        = (df$n_After_impact[df$Study.type == "Before After Control Impact"] 
                                                           + df$n_Before_impact[df$Study.type == "Before After Control Impact"])/2

# Standard deviations.
### Before After
df$SD1[df$Study.type == "Before After"]       = df$SD_Before_impact[df$Study.type == "Before After"]

### Control-Impact
df$SD2[df$Study.type == "Before After"]       = df$SD_After_impact[df$Study.type == "Before After"]

### BACI
df$SD1[df$Study.type == "Control Impact"]       = df$SD_After_control[df$Study.type == "Control Impact"]
df$SD2[df$Study.type == "Control Impact"]       = df$SD_After_impact[df$Study.type == "Control Impact"]

#### Before After Control Impact studies
df$SD1[df$Study.type == "Before After Control Impact"] = df$SD_After_control[df$Study.type == "Before After Control Impact"]
df$SD2[df$Study.type == "Before After Control Impact"] = df$SD_After_impact[df$Study.type == "Before After Control Impact"]

2.2 Calculate lnRR and VlnRR

Log response ratio is calculated as (1) : \(ln(\overline{x}_{2}/\overline{x}_{1})\) (1), with \(\overline{x}_{2}\) and \(\overline{x}_{1})\) the means of the studied variable in the impact vs. control, or after vs. before samples respectively.

VarLnRR is calculated by first calculating the pooled standard deviation \(S\) (2), \(S_{pooled} = \sqrt{\frac{(n_{1}-1)S^2_{1} + (n_{2}-1)S^2_{2}}{n_{1}+n_{2}-2}}\) (2) and using \(S\) to calculate the variance \(V_{D}\) (3). See Borenstein et al., 2009 \(V_{D} = \sqrt{\frac{S^2_{1}}{n_{1}} + \frac{S^2_{2}}{n_{2}}}\) (3)

# Adding a small value to 0's in responses to avoid NA's
df <- df[-which(df$response1 == 0),]
df <- df[-which(df$response2 == 0),]

# df$response2[which(df$response2 == 0)] <- 0.001
# df$response1[which(df$response1 == 0)] <- 0.001

# Calculate lnRR
df$lnRR = log(df$response2/df$response1)

# # Remove NaN log-responses (log(-n))
df     <- df[-which(is.nan(df$lnRR)),] 

# Change Infinities to NA --> X/0 or 0/x
df$lnRR[df$lnRR == Inf]  = NA  # log(x/0)
df$lnRR[df$lnRR == -Inf] = NA  # log(0/x)

nalnrr <- which(is.na(df$lnRR)) # Rows where no response ratio is calculated.

# Overwrite full df with df with NA rows removed.
# Finally DF is of length 2735.
remrows <- df[nalnrr,]
df      <- df[-nalnrr,]

fulldata <- df

2.3 Standard deviations and weights.

-> Variances lower than 0.01 are assigned a value of 0.01, because otherwise this leads to very large weights which tend to dominate the analysis.

# Pooled standard deviation
df$Spooled = sqrt(((df$n1 - 1) * (df$SD1^2) + (df$n2 - 1) * (df$SD2^2)) / (df$n1 + df$n2 - 2))

# VlnRR
df$VarLnRR = df$Spooled^2 * ( 1 / (df$n1 * df$response1^2) + 1 / (df$n2 * df$response2^2))

novarrows <- which(is.na(df$VarLnRR))
dfshort   <- df[-novarrows, ]

## Before After Control Impact [BACI] VarlnRR
## Calculation is different for BACI, prev. values are overwritten BACI rows.
dfshort$VarLnRR[dfshort$Study.type == "Before After Control Impact"] =
  dfshort$SD_After_control[dfshort$Study.type == "Before After Control Impact"]^2 / 
    (dfshort$n_After_control[dfshort$Study.type == "Before After Control Impact"] * dfshort$Mean_After_control[dfshort$Study.type == "Before After Control Impact"]^2) +
  
  dfshort$SD_Before_control[dfshort$Study.type=="Before After Control Impact"]^2 / 
    (dfshort$n_Before_control[dfshort$Study.type=="Before After Control Impact"] * dfshort$Mean_Before_control[dfshort$Study.type == "Before After Control Impact"]^2) + 
  
  dfshort$SD_After_impact[dfshort$Study.type == "Before After Control Impact"]^2 / 
    (dfshort$n_After_impact[dfshort$Study.type == "Before After Control Impact"] * dfshort$Mean_After_impact[dfshort$Study.type == "Before After Control Impact"]^2) +
  
  dfshort$SD_Before_impact[dfshort$Study.type == "Before After Control Impact"]^2 /
    (dfshort$n_Before_impact[dfshort$Study.type == "Before After Control Impact"] * dfshort$Mean_Before_impact[dfshort$Study.type == "Before After Control Impact"]^2)

# Remove those 3 NA's produced
dfshort   <- dfshort[-which(is.na(dfshort$VarLnRR)), ]
dfshort$VarLnRR[dfshort$VarLnRR < 0.01] <- 0.01

# Weight (W) for lnRR effect size
## This introduces Inf
dfshort$W    <- 1 / dfshort$VarLnRR
dfshort$WY   <- dfshort$W * dfshort$lnRR
dfshort$WY2  <- dfshort$W * dfshort$lnRR^2
dfshort$WYsquared <- dfshort$WY^2

2.4 Change names dfshort

-> For easier manipulation.

# Edit column names for to facilitate modelling.
colnames(dfshort)[colnames(dfshort) == "Response.variable"]                     <- "respvar"
colnames(dfshort)[colnames(dfshort) == "Sample.core.depth.slice"]               <- "slicedepth"
colnames(dfshort)[colnames(dfshort) == "Study.type"]                            <- "stype"
colnames(dfshort)[colnames(dfshort) == "Harmonized_study.type"]                 <- "hstype"
colnames(dfshort)[colnames(dfshort) == "Water.depth..m."]                       <- "watdepth"
colnames(dfshort)[colnames(dfshort) == "Trawling.effort_numerical_harmonized"]  <- "heffortnum"
colnames(dfshort)[colnames(dfshort) == "Trawling_effort_GFW"]                   <- "effortGFW"
colnames(dfshort)[colnames(dfshort) == "Model_Chl_bottom..mg.m3"]               <- "chlabot"
colnames(dfshort)[colnames(dfshort) == "Model_Current_velocity..m.s."]          <- "cvel"
colnames(dfshort)[colnames(dfshort) == "Model_Dissolved_Oxygen..mol.m3."]       <- "O2bot"
colnames(dfshort)[colnames(dfshort) == "Model_NO3_bottom..mol.m3."]             <- "nitbot"
colnames(dfshort)[colnames(dfshort) == "Model_PO4_bottom..mol.m3."]             <- "phosbot"
colnames(dfshort)[colnames(dfshort) == "Model_Sal_bottom"]                      <- "salbot"
colnames(dfshort)[colnames(dfshort) == "Model_Silicate_bottom..mol.m3."]        <- "silbot"
colnames(dfshort)[colnames(dfshort) == "Model_Temp_bottom"]                     <- "tempbot"
colnames(dfshort)[colnames(dfshort) == "Model_NPP_surface..g.m3.day."]          <- "nppsurf"
colnames(dfshort)[colnames(dfshort) == "dist_shore..m."]                        <- "shoredist"
colnames(dfshort)[colnames(dfshort) == "Time.since.trawl..days."]               <- "timesincetrawl"
colnames(dfshort)[colnames(dfshort) == "Time.since.first.disturbance..years."]  <- "timefirstdist"
colnames(dfshort)[colnames(dfshort) == "Habitat.type_harmonized"]               <- "hhabtype"
colnames(dfshort)[colnames(dfshort) == "Seasonality_harmonized"]                <- "hseason"
colnames(dfshort)[colnames(dfshort) == "Trawling.gear.type_harmonized"]         <- "gear"
colnames(dfshort)[colnames(dfshort) == "Historically.fished"]                   <- "histfished"
colnames(dfshort)[colnames(dfshort) == "Trawling.effort_categorical"]           <- "effortcat"
colnames(dfshort)[colnames(dfshort) == "Trawling.effort_units_harmonized"]      <- "heffortunits"
colnames(dfshort)[colnames(dfshort) == "Control_historically_trawled"]          <- "CTRLhisttrawled"

2.5 Saving two dataframes as separate entities.

save(file = "../r_objects/fulldata.rda", fulldata)
save(file = "../r_objects/shortdata.rda", dfshort)

load(file = "../r_objects/fulldata.rda")
load(file = "../r_objects/shortdata.rda")

3 ALL ROWS

3.1 Data wrangling

• Group OM (LOI) with TOC content
• Select variables with enough representation for further analysis
• Classify depth slices in “Surface” (0-2 cm), “Subsurface” (2-5 cm), “Deep” (5-10 cm), “Very deep” (> 10 cm), “Full sample” (e.g. 0-10 or “incubation core” etc.).

# Group OM (LOI) with TOC content.
fulldata$Response.variable[fulldata$Response.variable == "OM (LOI)"] <- "TOC"

vars <- sort(table(fulldata$Response.variable), decreasing = TRUE)
sel  <- names(vars)[c(1:12, 14, 15, 18, 19, 20, 21, 22, 26, 28)]
vars <- vars[sel]

tab <- table(fulldata$Sample.core.depth.slice, fulldata$Response.variable)
tab <- tab[,names(vars)]

fulldata$depths <- fulldata$Sample.core.depth.slice
fulldata$depths[fulldata$depths %in% c("0-0.5", "0-1", "0-2", "0-2.5", "0.5-1", "1-1.5", "1-2", "1.5-2", "surface sediment")]   = "Surface" # 0-2
fulldata$depths[fulldata$depths %in% c("0-3", "0-4", "0-5", "0-5.5", "1-3", "2-2.5", "2-3", "2-4", "2.5-3", "3-3.5", "3-4", "3-5", "3.5-4", "", "4-4.5", "4-5", "4.5-5")]   = "Subsurface"              # 2-5
fulldata$depths[fulldata$depths %in% c("4-6", "4-8", "5-10", "5-6", "5-7", "6-10", "6-7", "7-10", "7-8", "7-9", "8-9", "9-10")]   = "Deep"                    # 5-10
fulldata$depths[fulldata$depths %in% c("10-11", "10-12", "10-15", "11-12", "12-13", "13-14", "14-15", "15-16", "15-20", "16-17", "17-18", "18-19", "19-20", "20-21", "21-22", "22-23", "23-24", "24-25", "25-26", "26-27", "27-28", "28-29", "29-30", "30-31", "31-32", "32-33", "33-34", "34-35", "35-36", "36-37", "37-38", "38-39", "39-40", "40-41", "41-42", "42-43")]   = "Very deep"  # > 10
fulldata$depths[fulldata$depths %in% c("0-10", "Full core", "Grab sample", "in-situ incubations", "on-board incubations")]   = "Full sample"

fulldata$depths <- factor(fulldata$depths, levels = c("Full sample", "Very deep", "Deep", "Subsurface", "Surface"))
vars

## 
##                   TOC      Dry bulk density                TOC/TN 
##                   276                   271                   194 
##                    TN                  Silt Mud content (< 63 µm) 
##                   189                   168                   160 
##                  Clay                 Chl-a                  Sand 
##                   157                   150                   139 
##                    TC                   TIC                   NHx 
##                   120                    85                    65 
##         Phaeopigments          Total Pb-210         Phytopigments 
##                    54                    51                    45 
##                   DIC                   NOx       Mean grain size 
##                    38                    35                    34 
##              Porosity                  EHAA              NOx flux 
##                    24                    18                    14

3.2 Plotting

3.2.1 Plot histograms of amount of data per groups

-> Histograms showing the amount of samples (database lines) per variable per given factor (season, habitat type, gear type).

## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## i Please use `after_stat(count)` instead.

3.2.2 Plot individual parameters

–> Response ratios of variables with enough instances are plotted individually, per depth slice.

3.2.2.1 Chl a

3.2.2.2 Phaeopigments

3.2.2.3 Phytopigments

3.2.2.4 Silt

3.2.2.5 Clay

3.2.2.6 Sand

3.2.2.7 Mean grain size

3.2.2.8 Dry bulk density

==> I’m thinking that this may be an effect of handling the sediment somehow.

3.2.2.10 TC

3.2.2.11 TN

3.2.2.12 TOC/TN

3.2.2.13 NHx

3.2.2.14 NOx

3.2.2.15 Phosphate

3.2.3 Plot per groups

3.2.3.1 Chla - other - pigments

3.2.3.2 Sediment characteristics

3.2.3.3 TOC/TIC

3.2.3.4 BGC

3.2.3.5 Interesting selection

4 SELECTED ROWS

• Proceeding with the random effects assumption (Borenstein et al., chapter 12).

4.1 Summarizing data

• Calculates the mean and variance around true response ratio assuming a random effects model.
• Average effect size +- confidence interval is calculated per variable –> Not taking into account slice depths.

# Function to calculate average and tailed distributions for random effects assumption.

summarizer <- function(df) {
  
  dfn <- NULL
  
  for(i in (unique(df$respvar))){
    
    tt <- subset(df, df$respvar == i)
    DF <- sum(complete.cases(tt$lnRR), na.rm = T) - 1 # degrees of freedom for observations that can be used
    
    # Calculations for log response ratios (lnRR)
    Q  <- sum(tt$WY2, na.rm = T) - (sum(tt$WY, na.rm = T))^2/sum(tt$W, na.rm = T)
    C  <- sum(tt$W, na.rm = T) - sum(tt$W^2, na.rm = T)/sum(tt$W, na.rm = T)
    T2 <- pmax(0, (Q - DF)/C)       # tau squared (between studies variance) never negative
    tt$WRand  <- 1/(tt$W + T2)        # Weight = 1/variance within + variance between studies
    tt$WYRand <- tt$WRand * tt$lnRR
    
    v <- i
    M  <- round((sum(tt$WYRand, na.rm = T)/sum(tt$WRand, na.rm = T)), digits = 2) # summary EF calculation
    Vm <- 1/(sum(tt$WRand, na.rm = T))                              # variance
    SD <- sd(tt$lnRR, na.rm = T)
    SE <- sqrt(Vm)
    LL <- round(M - 1.96 * SE, digits = 2)
    UL <- round(M + 1.96 * SE, digits = 2)
    Z  <- M/SE
    pval <- 2*pnorm(q=Z, lower.tail=T) # p value for 2 tailed test
    n    <- length(tt$respvar)
    s    <- length(unique(tt$article_id))
    
    dfn <- rbind(dfn, data.frame(v, M, Vm, SD, SE, LL, UL, Z, pval, n, s))
    
  }
  return(dfn)
}

# Group OM (LOI) with TOC content.
dfshort$respvar[dfshort$respvar == "OM (LOI)"] <- "TOC"

# Checking relevant variables   
relvar <- sort(table(dfshort$respvar, dfshort$article_type_id), decreasing = TRUE)
relvar # TOC, Chl-a, TOC-TN, TN, Dry bulk density

##    [1] 44 39 32 25 20 17 16 16 16 15 15 15 13 13 13 13 13 13 13 13 13 12 12 12
##   [25] 12 12 10 10 10 10  9  9  9  9  9  9  9  9  8  8  8  8  8  8  7  7  7  6
##   [49]  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
##   [73]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  4  4  4  4  4  4
##   [97]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3  3  3
##  [121]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
##  [145]  3  3  3  3  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
##  [169]  2  2  2  2  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [193]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
##  [217]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0
##  [241]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [265]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [289]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [313]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [337]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [361]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [385]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [409]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [433]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [457]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [481]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [505]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [529]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [553]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [577]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [601]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [625]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [649]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [673]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [697]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [721]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [745]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [769]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [793]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [817]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [841]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [865]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [889]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [913]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [937]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [961]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
##  [985]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1009]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1033]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1057]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1081]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1105]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1129]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1153]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1177]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1201]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1225]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1249]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1273]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1297]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1321]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1345]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1369]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1393]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1417]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1441]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1465]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1489]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1513]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1537]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1561]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1585]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1609]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1633]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1657]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1681]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1705]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1729]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1753]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1777]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1801]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1825]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1849]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1873]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1897]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1921]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1945]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1969]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [1993]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2017]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2041]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2065]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2089]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2113]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2137]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2161]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2185]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2209]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2233]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2257]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2281]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2305]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2329]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2353]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2377]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2401]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2425]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2449]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2473]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2497]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2521]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2545]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2569]  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
## [2593]  0  0  0  0  0  0  0  0

# Check relevant variables 2
counteach <- plyr::count(dfshort[,c(1,9)])
counteach <- plyr::count(counteach[,1])
counteach <- counteach[order(counteach$freq, decreasing = TRUE),]
relvar2 <- counteach$x[counteach$freq >= 3]
relvar2 <- relvar2[c(2, 6, 7, 1, 3, 4, 17, 5, 23, 15, 10, 20, 16, 9, 8, 11, 14, 13, 22, 12, 18, 19, 21)]
colz    <- c(rep("chartreuse2", 3), rep("darkseagreen4", 4), rep("bisque3", 6), rep("cadetblue3", 6), rep("darkorange3", 4))

relv <- names(relvar)[1:21]
# vars <- names(tab)[20:25]

dfshort$depths <- dfshort$slicedepth


# Average these
dfsummed <- summarizer(dfshort)

# Plotting summarizer results.
subover <- dfsummed[c(8,9,3,11,30, 2,4,26, 10,36,41,48),]
subover$v <- factor(subover$v, levels = c(subover$v))
subbgc  <- dfsummed[-c(8,9,3,11,30, 2,4,26, 10,36,41,48),]
subbgc  <- subbgc[subbgc$n >= 3,]
subbgc  <- subbgc[c(1,25,36,37,7,14,15, 6,9,10,22,23,24),]   # oxygen / c turnover -- BGC content -- Others

# New subover more selected
suball     <- dfsummed[dfsummed$v %in% relvar2[c(1:14)],]
suball     <- suball[match(relvar2[c(1:14)], suball$v),]
suball$v   <- factor(suball$v, levels = suball$v)
colall     <- colz[c(1:14)]

subchlatoc   <- dfsummed[dfsummed$v %in% relvar2[c(1:7)],] 
subchlatoc   <- subchlatoc[match(relvar2[c(1:7)], subchlatoc$v),]
subchlatoc$v <- factor(subchlatoc$v, levels = subchlatoc$v)
colchlatoc   <- colz[c(1:7)]

subsed     <- dfsummed[dfsummed$v %in% relvar2[c(8:13)],] 
subsed     <- subsed[match(relvar2[c(8:13)], subsed$v),]
subsed$v   <- factor(subsed$v, levels = subsed$v)
colsed     <- colz[c(8:13)]

subflux    <- dfsummed[dfsummed$v %in% relvar2[c(14:19)],] 
subflux    <- subflux[match(relvar2[c(14:19)], subflux$v),]
subflux$v  <- factor(subflux$v, levels = subflux$v)
colflux    <- colz[c(14:19)]

subrest    <- dfsummed[dfsummed$v %in% relvar2[c(20:23)],] 
subrest    <- subrest[match(relvar2[c(20:23)], subrest$v),]
subrest$v  <- factor(subrest$v, levels = subrest$v)
colrest    <- colz[c(20:23)]

4.2 Plotting main important parameters (old)

• Long plotting function to get something resembling a forest plot.

4.3 Plotting secondary important parameters (old)

4.4 Plot subsets with colours (new)

4.4.1 All new subset

4.4.2 Organic matter stuff (old)

4.4.3 Sediment stuff

4.4.4 Flux stuff

5 Meta-modelling

• Using the shortened dataset.

5.1 Investigating depth slices + combinations with other factors

• Group TOC and OM (LOI) for modelling / visualisation.
• Check out no. instances of each variable.
• Reclassify depth slices into classes (same as above).

# join TOC and OM-LOI
dfshort$respvar[dfshort$respvar == "OM (LOI)"] <- "TOC"

# Selecting relevant variables   
tab <- sort(table(dfshort$respvar), decreasing = TRUE)
tab # TOC, Chl-a, TOC-TN, TN, Dry bulk density

## 
##                             TOC                           Chl-a 
##                             190                             136 
##                          TOC/TN                              TN 
##                              80                              76 
##                   Phaeopigments                   Phytopigments 
##                              54                              45 
##                             NHx                   Excess Pb-210 
##                              43                              29 
##                            Silt                 Mean grain size 
##                              28                              26 
##                Dry bulk density                        Proteins 
##                              25                              24 
##           Mud content (< 63 µm)                        NHx flux 
##                              21                              21 
##                              TC              Oxygen consumption 
##                              20                              19 
##                            EHAA                       Phosphate 
##                              18                              18 
##                            Clay                   Water content 
##                              17                              17 
##                             DIC                            Sand 
##                              15                              15 
##                             NOx                        NOx flux 
##                              14                              14 
##                             TIC                        Porosity 
##                              13                              12 
##           Sediment Surface Area                             P/C 
##                              12                              10 
##            Biopolymeric C (BPC)                   Carbohydrates 
##                               9                               9 
##                          Lipids           Algal fraction of BPC 
##                               9                               8 
##                     OM turnover                Protein turnover 
##                               8                               8 
##                              TP                             DOU 
##                               8                               7 
##                  Phosphate flux                    Total Pb-210 
##                               7                               7 
##                           L-PO4                   Silicate flux 
##                               6                               6 
##                              TS                              IP 
##                               5                               4 
##                             OPD                         Sorting 
##                               4                               4 
##                  Very fine sand          ?-glucosidase activity 
##                               4                               3 
##         aminopeptidase activity          bacterial C production 
##                               3                               3 
##           carbohydrate turnover                        Gross PP 
##                               3                               3 
##                   Hydrogen flux                      Net N flux 
##                               3                               3 
##                        Silicate         Bacteria uptake of C-13 
##                               3                               2 
##                            C-13                     C oxidation 
##                               1                               1 
##                           CaCO3         Excess Pb-210 inventory 
##                               1                               1 
## Excess Pb-210 penetration depth         Excess Th-234 inventory 
##                               1                               1 
## Excess Th-234 penetration depth                          Gravel 
##                               1                               1 
##                            N-15         Sediment uptake of C-13 
##                               1                               1 
##                             SML 
##                               1

vars <- names(tab)[1:21]
# vars <- names(tab)[20:25]

dfshort$depths <- dfshort$slicedepth

dfshort$depths[dfshort$depths %in% c("0-0.5", "0-1", "0-2", "0-2.5", "0.5-1", "1-1.5", "1-2", "1.5-2", "surface sediment")]   = "Surface" # 0-2
dfshort$depths[dfshort$depths %in% c("0-3", "0-4", "0-5", "0-5.5", "1-3", "2-2.5", "2-3", "2-4", "2.5-3", "3-3.5", "3-4", "3-5", "3.5-4", "", "4-4.5", "4-5", "4.5-5")]   = "Subsurface"              # 2-5
dfshort$depths[dfshort$depths %in% c("4-6", "4-8", "5-10", "5-6", "5-7", "6-10", "6-7", "7-10", "7-8", "7-9", "8-9", "9-10")]   = "Deep"                    # 5-10
dfshort$depths[dfshort$depths %in% c("10-11", "10-12", "10-15", "11-12", "12-13", "13-14", "14-15", "15-16", "15-20", "16-17", "17-18", "18-19", "19-20", "20-21", "21-22", "22-23", "23-24", "24-25", "25-26", "26-27", "27-28", "28-29", "29-30", "30-31", "31-32", "32-33", "33-34", "34-35", "35-36", "36-37", "37-38", "38-39", "39-40", "40-41", "41-42", "42-43")]   = "Very deep"  # > 10
dfshort$depths[dfshort$depths %in% c("0-10", "Full core", "Grab sample", "in-situ incubations", "on-board incubations")]   = "Full sample"
dfshort$depths <- factor(dfshort$depths, levels = c("Surface", "Subsurface", "Deep", "Very deep", "Full sample"))

table(dfshort$depths, dfshort$article_type_id)

##              
##               0.1 0.2  2  5  8 11 19 20 23 33 65.2 66 68 69 70 71 96 100 111
##   Surface       4   8  8  0  6 17  0 14 32 12    0  0  0 30  4  4  0   0   7
##   Subsurface    0   0 12  0  0 21  0 25 16 20    2  6  0 40  6  6  0   0   0
##   Deep          0   0 20  1  0  5  0 31  4 17    0  0  0 64  2  9  0   0   0
##   Very deep     0   0 18  0  0  0  0 18  2  2    0  0  0 65  8  0  0   0   0
##   Full sample   9  18  9  4 15  0 28  0 25  0    0  0 40  1  0  0 24   2   0
##              
##               115 121 129.1 129.2 132 138 151 152.1 152.2 152.3 154 155 163 164
##   Surface       0   9    15    16  10   0   2     1     1     0   0  45  12  19
##   Subsurface    0   0    21    24   0   6   0     2     2     2   0   0   0  21
##   Deep          0   0     0     0   0   0   0     0     0     0   0   0  12  16
##   Very deep     0   0     0     0   0   0   0     0     0     0   0   0   0  16
##   Full sample   4   0     0     0   0   0   0     0     0     0   9   0   0   0
##              
##               165 167 170 181 184.1 184.2 194
##   Surface       0  16   8  39    10     4   6
##   Subsurface    1  16   0   0    15     4   0
##   Deep          0   0   0   0    20     7   0
##   Very deep     0   0   0   0     0     8   0
##   Full sample   0   0   0   0     0     0   2

table(dfshort$hseason, dfshort$article_type_id)

##         
##          0.1 0.2   2   5   8  11  19  20  23  33 65.2  66  68  69  70  71  96
##   Autumn   0   0  67   5  14   0   0   0   0   0    0   0   0 200   0   0   0
##   Dry      0   0   0   0   0   0  21   0   0   0    0   0   0   0   0   0  12
##   Spring   0   0   0   0   0  43   0   0   0   0    0   0   8   0   0  19   0
##   Summer  13  26   0   0   7   0   0  88  79  51    2   0  32   0   0   0   0
##   Wet      0   0   0   0   0   0   7   0   0   0    0   0   0   0   0   0  12
##   Winter   0   0   0   0   0   0   0   0   0   0    0   0   0   0   0   0   0
##   Yearly   0   0   0   0   0   0   0   0   0   0    0   6   0   0  20   0   0
##         
##          100 111 115 121 129.1 129.2 132 138 151 152.1 152.2 152.3 154 155 163
##   Autumn   0   0   0   3     0     0  10   0   0     0     0     0   0   0   0
##   Dry      1   0   0   0     0     0   0   0   0     0     0     0   0   0   0
##   Spring   0   7   4   0     0     0   0   0   0     0     0     0   6   0  16
##   Summer   0   0   0   3    36    40   0   0   2     3     3     2   3  45   0
##   Wet      1   0   0   0     0     0   0   0   0     0     0     0   0   0   0
##   Winter   0   0   0   3     0     0   0   6   0     0     0     0   0   0   8
##   Yearly   0   0   0   0     0     0   0   0   0     0     0     0   0   0   0
##         
##          164 165 167 170 181 184.1 184.2 194
##   Autumn   0   0   8   4  13     0     0   0
##   Dry      0   0   0   0   0     0     0   0
##   Spring  26   0   8   4   0    45    23   0
##   Summer  46   1   8   0  26     0     0   4
##   Wet      0   0   0   0   0     0     0   0
##   Winter   0   0   8   0   0     0     0   4
##   Yearly   0   0   0   0   0     0     0   0

# Datasets for known times and known trawling intensity.
## known times
timedf <- subset(dfshort, !is.na(dfshort$timesincetrawl))  # select known trawl times

## known fishing intensity
fishdf <- subset(dfshort, !is.na(dfshort$heffortnum))        # first take out unknown for recovery models

## All shortened data.
all <- dfshort


# Print output of all / make tables

# table(dfshort$depths[dfshort$respvar == "TOC"], dfshort$article_type_id[dfshort$respvar == "TOC"] )
# 
# for(i in vars){
#   print(i)
#   print(table(dfshort$depths[dfshort$respvar == i], dfshort$article_type_id[dfshort$respvar == i] )  )
#   # print(table(dfshort$depths[dfshort$respvar == i], dfshort$hseason[dfshort$respvar == i] )  )
#   # print(table(dfshort$depths[dfshort$respvar == i], dfshort$histfished[dfshort$respvar == i] )  )
# }

5.2 Averaging per depth slice per study per variable.

# Function to calculate average and tailed distributions for random effects assumption.

# TODEBUG
# df <- dfshort
# i  <- unique(dfshort$respvar)[2]
# tt <- subset(df, df$respvar == i)
# j  <- unique(tt$depths)[1]

# Remove TODEBUG

summarizer_varslice <- function(df, varz) {
  
  dfn <- NA
  
  for(i in (unique(varz))){
    
    tt <- subset(df, df$respvar == i)
    
    for(j in (unique(tt$depths))){
      
      dd <- subset(tt, tt$depths == j)
      dd <- escalc(data = dd, yi = lnRR, vi = VarLnRR)
      varcov <- vcalc(vi = vi, cluster = article_type_id, data = dd, nearpd = TRUE)
      avg <- aggregate(x = dd, cluster = article_type_id, V = varcov, struct = "CS", weighted = FALSE)

      dfn <- rbind(dfn, avg)
      
          }
  }
  return(dfn)
}

out <- summarizer_varslice(dfshort, varz = vars)

out$depths <- factor(out$depths, levels = c("Surface", "Subsurface", "Deep", "Very deep", "Full sample"))
out <- out[order(out$respvar, out$yi, decreasing = TRUE),]

5.3 Individual variables

–> The dataframe “out” now contains averages per slicedepth per variable per study (whew). –> Now, for each variable, we can calculate the averages per slicedepth, and plotting this.

–> Based on the checkout table, we can definitely work with TOC, Chl-a, Mean grain size, Mud content, NHx, Phaeopigments, phytopigments, proteins, TN, TOC/TN, and Silt.

checkout <- table(out$depths, out$respvar)
checkout

##              
##               Chl-a Clay DIC Dry bulk density EHAA Excess Pb-210
##   Surface        16    2   1                1    2             2
##   Subsurface      7    2   1                1    1             2
##   Deep            5    1   1                1    2             2
##   Very deep       3    1   0                1    1             1
##   Full sample     2    0   0                0    0             0
##              
##               Mean grain size Mud content (< 63 µm) NHx NHx flux
##   Surface                   5                     1   3        0
##   Subsurface                6                     4   3        0
##   Deep                      0                     1   3        0
##   Very deep                 0                     1   1        0
##   Full sample               2                     2   1        7
##              
##               Oxygen consumption Phaeopigments Phosphate Phytopigments Proteins
##   Surface                      0            10         1             8        5
##   Subsurface                   0             6         2             6        3
##   Deep                         0             3         1             3        3
##   Very deep                    0             2         0             1        1
##   Full sample                  7             0         0             0        0
##              
##               Silt TC TN TOC TOC/TN Water content
##   Surface        3  1 10  23     11             1
##   Subsurface     3  2  7  15      6             1
##   Deep           2  2  3   8      3             1
##   Very deep      2  1  3   6      2             1
##   Full sample    0  1  5   7      5             1

5.3.1 TOC

5.3.1.1 Slice depth significance

• Slices not significant overall predictor of TOC lnRR (p-val = 0.2033) according to QM.
• surface mean does differ from 0 (p = 0.0276)

## 
## Mixed-Effects Model (k = 59; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0467 (SE = 0.0142)
## tau (square root of estimated tau^2 value):             0.2161
## I^2 (residual heterogeneity / unaccounted variability): 71.91%
## H^2 (unaccounted variability / sampling variability):   3.56
## R^2 (amount of heterogeneity accounted for):            7.15%
## 
## Test for Residual Heterogeneity:
## QE(df = 54) = 200.1247, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 5.9454, p-val = 0.2033
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb    ci.ub    
## intrcpt             -0.1300  0.0590  -2.2035  0.0276  -0.2456  -0.0144  * 
## depthsSubsurface     0.1219  0.0941   1.2954  0.1952  -0.0625   0.3062    
## depthsDeep           0.0921  0.1127   0.8177  0.4135  -0.1287   0.3130    
## depthsVery deep      0.0282  0.1302   0.2169  0.8283  -0.2269   0.2834    
## depthsFull sample    0.2886  0.1253   2.3044  0.0212   0.0431   0.5341  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 59; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0467 (SE = 0.0142)
## tau (square root of estimated tau^2 value):             0.2161
## I^2 (residual heterogeneity / unaccounted variability): 71.91%
## H^2 (unaccounted variability / sampling variability):   3.56
## 
## Test for Residual Heterogeneity:
## QE(df = 54) = 200.1247, p-val < .0001
## 
## Test of Moderators (coefficients 1:5):
## QM(df = 5) = 7.8530, p-val = 0.1645
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb    ci.ub    
## depthsSurface       -0.1300  0.0590  -2.2035  0.0276  -0.2456  -0.0144  * 
## depthsSubsurface    -0.0081  0.0733  -0.1106  0.9120  -0.1517   0.1355    
## depthsDeep          -0.0378  0.0960  -0.3939  0.6936  -0.2260   0.1504    
## depthsVery deep     -0.1017  0.1161  -0.8764  0.3808  -0.3292   0.1258    
## depthsFull sample    0.1587  0.1105   1.4360  0.1510  -0.0579   0.3753    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.2 Time since trawling

• modTOCtst: Model not significant (p = 0.0868)

## 
## Multivariate Meta-Analysis Model (k = 83; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0523  0.2286     20     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 75) = 225.9237, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 15.0857, p-val = 0.0349
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub     
## intrcpt                  -0.1799  0.0851  -2.1136  0.0345  -0.3467  -0.0131   * 
## depthsSubsurface          0.2333  0.0888   2.6257  0.0086   0.0591   0.4074  ** 
## depthsDeep                0.2479  0.1127   2.1992  0.0279   0.0270   0.4689   * 
## depthsFull sample         0.2566  0.1671   1.5355  0.1247  -0.0709   0.5842     
## name                      0.0643  0.0449   1.4317  0.1522  -0.0237   0.1523     
## depthsSubsurface:name    -0.0960  0.0564  -1.7016  0.0888  -0.2065   0.0146   . 
## depthsDeep:name          -0.0619  0.1191  -0.5201  0.6030  -0.2953   0.1714     
## depthsFull sample:name   -0.2251  0.1007  -2.2359  0.0254  -0.4225  -0.0278   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 89; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0521  0.2282     20     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 79) = 227.7130, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 15.1541, p-val = 0.0868
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     -0.1787  0.0848  -2.1065  0.0352 
## depthsSubsurface                             0.2335  0.0888   2.6291  0.0086 
## depthsDeep                                   0.2495  0.1124   2.2190  0.0265 
## depthsVery deep                              0.1813  0.1548   1.1710  0.2416 
## depthsFull sample                            0.2556  0.1669   1.5316  0.1256 
## log(timesincetrawl + 1)                      0.0639  0.0449   1.4238  0.1545 
## depthsSubsurface:log(timesincetrawl + 1)    -0.0959  0.0564  -1.7016  0.0888 
## depthsDeep:log(timesincetrawl + 1)          -0.0636  0.1188  -0.5357  0.5922 
## depthsVery deep:log(timesincetrawl + 1)     -0.0519  0.1219  -0.4261  0.6701 
## depthsFull sample:log(timesincetrawl + 1)   -0.2248  0.1006  -2.2344  0.0255 
##                                              ci.lb    ci.ub     
## intrcpt                                    -0.3449  -0.0124   * 
## depthsSubsurface                            0.0594   0.4075  ** 
## depthsDeep                                  0.0291   0.4699   * 
## depthsVery deep                            -0.1221   0.4847     
## depthsFull sample                          -0.0715   0.5826     
## log(timesincetrawl + 1)                    -0.0240   0.1518     
## depthsSubsurface:log(timesincetrawl + 1)   -0.2064   0.0146   . 
## depthsDeep:log(timesincetrawl + 1)         -0.2965   0.1692     
## depthsVery deep:log(timesincetrawl + 1)    -0.2908   0.1869     
## depthsFull sample:log(timesincetrawl + 1)  -0.4220  -0.0276   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 89; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0521  0.2282     20     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 79) = 227.7130, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 17.0179, p-val = 0.0740
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## depthsSurface                               -0.1787  0.0848  -2.1065  0.0352 
## depthsSubsurface                             0.0548  0.1024   0.5349  0.5927 
## depthsDeep                                   0.0708  0.1258   0.5629  0.5735 
## depthsVery deep                              0.0026  0.1642   0.0159  0.9873 
## depthsFull sample                            0.0769  0.1484   0.5180  0.6045 
## log(timesincetrawl + 1)                      0.0639  0.0449   1.4238  0.1545 
## depthsSubsurface:log(timesincetrawl + 1)    -0.0959  0.0564  -1.7016  0.0888 
## depthsDeep:log(timesincetrawl + 1)          -0.0636  0.1188  -0.5357  0.5922 
## depthsVery deep:log(timesincetrawl + 1)     -0.0519  0.1219  -0.4261  0.6701 
## depthsFull sample:log(timesincetrawl + 1)   -0.2248  0.1006  -2.2344  0.0255 
##                                              ci.lb    ci.ub    
## depthsSurface                              -0.3449  -0.0124  * 
## depthsSubsurface                           -0.1460   0.2556    
## depthsDeep                                 -0.1758   0.3175    
## depthsVery deep                            -0.3192   0.3245    
## depthsFull sample                          -0.2140   0.3678    
## log(timesincetrawl + 1)                    -0.0240   0.1518    
## depthsSubsurface:log(timesincetrawl + 1)   -0.2064   0.0146  . 
## depthsDeep:log(timesincetrawl + 1)         -0.2965   0.1692    
## depthsVery deep:log(timesincetrawl + 1)    -0.2908   0.1869    
## depthsFull sample:log(timesincetrawl + 1)  -0.4220  -0.0276  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.3 Trawling frequency

• modTOCtfr: model not significant, not checking individual regressions.

## 
## Multivariate Meta-Analysis Model (k = 96; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0472  0.2172     19     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 86) = 168.1157, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 18.6119, p-val = 0.0287
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub     
## intrcpt                  -0.1378  0.0741  -1.8586  0.0631  -0.2831   0.0075   . 
## depthsSubsurface          0.1236  0.0549   2.2515  0.0244   0.0160   0.2313   * 
## depthsDeep                0.1853  0.0648   2.8605  0.0042   0.0583   0.3123  ** 
## depthsVery deep           0.1208  0.1129   1.0698  0.2847  -0.1005   0.3421     
## depthsFull sample         0.2143  0.2597   0.8249  0.4094  -0.2948   0.7233     
## name                     -0.0001  0.0025  -0.0535  0.9574  -0.0049   0.0047     
## depthsSubsurface:name    -0.0023  0.0011  -2.1592  0.0308  -0.0045  -0.0002   * 
## depthsDeep:name          -0.0028  0.0011  -2.5956  0.0094  -0.0049  -0.0007  ** 
## depthsVery deep:name     -0.0007  0.0014  -0.5033  0.6148  -0.0035   0.0020     
## depthsFull sample:name   -0.0483  0.0422  -1.1468  0.2515  -0.1310   0.0343     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0466  0.2160     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 66) = 131.5582, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 11.2702, p-val = 0.2576
## 
## Model Results:
## 
##                                   estimate      se     zval    pval    ci.lb 
## intrcpt                            -0.1609  0.0790  -2.0372  0.0416  -0.3156 
## depthsSubsurface                    0.1132  0.0547   2.0680  0.0386   0.0059 
## depthsDeep                          0.1796  0.0647   2.7737  0.0055   0.0527 
## depthsVery deep                     0.1156  0.1133   1.0202  0.3077  -0.1065 
## depthsFull sample                   0.0985  0.1855   0.5312  0.5953  -0.2650 
## timesincetrawl                      0.0001  0.0009   0.1462  0.8838  -0.0015 
## depthsSubsurface:timesincetrawl     0.0003  0.0011   0.3134  0.7540  -0.0017 
## depthsDeep:timesincetrawl          -0.0003  0.0033  -0.0965  0.9231  -0.0067 
## depthsVery deep:timesincetrawl      0.0004  0.0034   0.1066  0.9151  -0.0063 
## depthsFull sample:timesincetrawl   -0.0049  0.0041  -1.1864  0.2354  -0.0130 
##                                     ci.ub     
## intrcpt                           -0.0061   * 
## depthsSubsurface                   0.2205   * 
## depthsDeep                         0.3064  ** 
## depthsVery deep                    0.3377     
## depthsFull sample                  0.4621     
## timesincetrawl                     0.0018     
## depthsSubsurface:timesincetrawl    0.0024     
## depthsDeep:timesincetrawl          0.0061     
## depthsVery deep:timesincetrawl     0.0070     
## depthsFull sample:timesincetrawl   0.0032     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0466  0.2160     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 66) = 131.5582, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 13.1843, p-val = 0.2135
## 
## Model Results:
## 
##                                   estimate      se     zval    pval    ci.lb 
## depthsSurface                      -0.1609  0.0790  -2.0372  0.0416  -0.3156 
## depthsSubsurface                   -0.0477  0.0839  -0.5684  0.5697  -0.2120 
## depthsDeep                          0.0187  0.0920   0.2034  0.8388  -0.1615 
## depthsVery deep                    -0.0453  0.1304  -0.3472  0.7285  -0.3009 
## depthsFull sample                  -0.0623  0.1724  -0.3614  0.7178  -0.4003 
## timesincetrawl                      0.0001  0.0009   0.1462  0.8838  -0.0015 
## depthsSubsurface:timesincetrawl     0.0003  0.0011   0.3134  0.7540  -0.0017 
## depthsDeep:timesincetrawl          -0.0003  0.0033  -0.0965  0.9231  -0.0067 
## depthsVery deep:timesincetrawl      0.0004  0.0034   0.1066  0.9151  -0.0063 
## depthsFull sample:timesincetrawl   -0.0049  0.0041  -1.1864  0.2354  -0.0130 
##                                     ci.ub    
## depthsSurface                     -0.0061  * 
## depthsSubsurface                   0.1167    
## depthsDeep                         0.1989    
## depthsVery deep                    0.2103    
## depthsFull sample                  0.2756    
## timesincetrawl                     0.0018    
## depthsSubsurface:timesincetrawl    0.0024    
## depthsDeep:timesincetrawl          0.0061    
## depthsVery deep:timesincetrawl     0.0070    
## depthsFull sample:timesincetrawl   0.0032    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.4 Current velocity

• modTOCcur: Sig. model fit (p < 0.0001).
• modTOCcuri: cvel-related combinations not significant.

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0853  0.2921     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.7064, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 30.8426, p-val = 0.0003
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub    
## intrcpt                  -0.2873  0.1177  -2.4418  0.0146  -0.5179  -0.0567  * 
## depthsSubsurface          0.0119  0.0784   0.1522  0.8790  -0.1418   0.1656    
## depthsDeep               -0.0310  0.0743  -0.4176  0.6763  -0.1766   0.1146    
## depthsVery deep           0.0825  0.1195   0.6905  0.4899  -0.1517   0.3168    
## depthsFull sample         0.6074  0.2870   2.1163  0.0343   0.0449   1.1700  * 
## name                      2.1453  1.3941   1.5389  0.1238  -0.5870   4.8777    
## depthsSubsurface:name     1.3756  1.2601   1.0916  0.2750  -1.0943   3.8454    
## depthsDeep:name           1.9641  1.1958   1.6426  0.1005  -0.3795   4.3078    
## depthsVery deep:name      0.7746  2.4924   0.3108  0.7560  -4.1104   5.6596    
## depthsFull sample:name   -3.9288  2.1890  -1.7948  0.0727  -8.2192   0.3616  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0853  0.2921     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.7064, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 30.8426, p-val = 0.0003
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub    
## intrcpt                  -0.2873  0.1177  -2.4418  0.0146  -0.5179  -0.0567  * 
## depthsSubsurface          0.0119  0.0784   0.1522  0.8790  -0.1418   0.1656    
## depthsDeep               -0.0310  0.0743  -0.4176  0.6763  -0.1766   0.1146    
## depthsVery deep           0.0825  0.1195   0.6905  0.4899  -0.1517   0.3168    
## depthsFull sample         0.6074  0.2870   2.1163  0.0343   0.0449   1.1700  * 
## cvel                      2.1453  1.3941   1.5389  0.1238  -0.5870   4.8777    
## depthsSubsurface:cvel     1.3756  1.2601   1.0916  0.2750  -1.0943   3.8454    
## depthsDeep:cvel           1.9641  1.1958   1.6426  0.1005  -0.3795   4.3078    
## depthsVery deep:cvel      0.7746  2.4924   0.3108  0.7560  -4.1104   5.6596    
## depthsFull sample:cvel   -3.9288  2.1890  -1.7948  0.0727  -8.2192   0.3616  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0853  0.2921     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.7064, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 31.0107, p-val = 0.0006
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub     
## depthsSurface            -0.2873  0.1177  -2.4418  0.0146  -0.5179  -0.0567   * 
## depthsSubsurface         -0.2754  0.1137  -2.4209  0.0155  -0.4983  -0.0524   * 
## depthsDeep               -0.3183  0.1229  -2.5896  0.0096  -0.5592  -0.0774  ** 
## depthsVery deep          -0.2048  0.1497  -1.3676  0.1714  -0.4982   0.0887     
## depthsFull sample         0.3201  0.2605   1.2287  0.2192  -0.1905   0.8308     
## cvel                      2.1453  1.3941   1.5389  0.1238  -0.5870   4.8777     
## depthsSubsurface:cvel     1.3756  1.2601   1.0916  0.2750  -1.0943   3.8454     
## depthsDeep:cvel           1.9641  1.1958   1.6426  0.1005  -0.3795   4.3078     
## depthsVery deep:cvel      0.7746  2.4924   0.3108  0.7560  -4.1104   5.6596     
## depthsFull sample:cvel   -3.9288  2.1890  -1.7948  0.0727  -8.2192   0.3616   . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.5 Water depth

• modTOCwat: significant model fit.
• modTOCwati: Significant regressions for surface subsurface, deep, and full sample.

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0619  0.2488     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 180) = 583.3072, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 37.4853, p-val < .0001
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                  -0.1868  0.0640  -2.9175  0.0035  -0.3123  -0.0613 
## depthsSubsurface          0.1312  0.0516   2.5415  0.0110   0.0300   0.2323 
## depthsDeep                0.1994  0.0577   3.4574  0.0005   0.0864   0.3124 
## depthsVery deep           0.1862  0.0660   2.8214  0.0048   0.0569   0.3156 
## depthsFull sample        -0.2897  0.2116  -1.3691  0.1710  -0.7045   0.1250 
## name                      0.0007  0.0003   2.6424  0.0082   0.0002   0.0013 
## depthsSubsurface:name    -0.0002  0.0001  -1.3281  0.1841  -0.0005   0.0001 
## depthsDeep:name          -0.0004  0.0001  -2.7097  0.0067  -0.0007  -0.0001 
## depthsVery deep:name     -0.0002  0.0002  -1.4603  0.1442  -0.0005   0.0001 
## depthsFull sample:name    0.0214  0.0069   3.0852  0.0020   0.0078   0.0350 
##                             
## intrcpt                  ** 
## depthsSubsurface          * 
## depthsDeep              *** 
## depthsVery deep          ** 
## depthsFull sample           
## name                     ** 
## depthsSubsurface:name       
## depthsDeep:name          ** 
## depthsVery deep:name        
## depthsFull sample:name   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0619  0.2488     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 180) = 583.3072, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 37.4853, p-val < .0001
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                      -0.1868  0.0640  -2.9175  0.0035  -0.3123  -0.0613 
## depthsSubsurface              0.1312  0.0516   2.5415  0.0110   0.0300   0.2323 
## depthsDeep                    0.1994  0.0577   3.4574  0.0005   0.0864   0.3124 
## depthsVery deep               0.1862  0.0660   2.8214  0.0048   0.0569   0.3156 
## depthsFull sample            -0.2897  0.2116  -1.3691  0.1710  -0.7045   0.1250 
## watdepth                      0.0007  0.0003   2.6424  0.0082   0.0002   0.0013 
## depthsSubsurface:watdepth    -0.0002  0.0001  -1.3281  0.1841  -0.0005   0.0001 
## depthsDeep:watdepth          -0.0004  0.0001  -2.7097  0.0067  -0.0007  -0.0001 
## depthsVery deep:watdepth     -0.0002  0.0002  -1.4603  0.1442  -0.0005   0.0001 
## depthsFull sample:watdepth    0.0214  0.0069   3.0852  0.0020   0.0078   0.0350 
##                                 
## intrcpt                      ** 
## depthsSubsurface              * 
## depthsDeep                  *** 
## depthsVery deep              ** 
## depthsFull sample               
## watdepth                     ** 
## depthsSubsurface:watdepth       
## depthsDeep:watdepth          ** 
## depthsVery deep:watdepth        
## depthsFull sample:watdepth   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0619  0.2488     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 180) = 583.3072, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 37.7393, p-val < .0001
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb    ci.ub 
## depthsSurface                -0.1868  0.0640  -2.9175  0.0035  -0.3123  -0.0613 
## depthsSubsurface             -0.0556  0.0693  -0.8031  0.4219  -0.1914   0.0801 
## depthsDeep                    0.0126  0.0750   0.1679  0.8667  -0.1344   0.1596 
## depthsVery deep              -0.0006  0.0821  -0.0072  0.9942  -0.1615   0.1603 
## depthsFull sample            -0.4765  0.2045  -2.3297  0.0198  -0.8774  -0.0756 
## watdepth                      0.0007  0.0003   2.6424  0.0082   0.0002   0.0013 
## depthsSubsurface:watdepth    -0.0002  0.0001  -1.3281  0.1841  -0.0005   0.0001 
## depthsDeep:watdepth          -0.0004  0.0001  -2.7097  0.0067  -0.0007  -0.0001 
## depthsVery deep:watdepth     -0.0002  0.0002  -1.4603  0.1442  -0.0005   0.0001 
## depthsFull sample:watdepth    0.0214  0.0069   3.0852  0.0020   0.0078   0.0350 
##                                
## depthsSurface               ** 
## depthsSubsurface               
## depthsDeep                     
## depthsVery deep                
## depthsFull sample            * 
## watdepth                    ** 
## depthsSubsurface:watdepth      
## depthsDeep:watdepth         ** 
## depthsVery deep:watdepth       
## depthsFull sample:watdepth  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.6 Primary productivity

• modTOCnpp: Significant model fit
• modTOCnppi: Significant model fits for all interaction levels.

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0797  0.2823     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.2973, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 25.2282, p-val = 0.0027
## 
## Model Results:
## 
##                         estimate      se     zval    pval     ci.lb    ci.ub    
## intrcpt                  -0.0495  0.0758  -0.6530  0.5138   -0.1982   0.0991    
## depthsSubsurface          0.0244  0.0478   0.5100  0.6101   -0.0693   0.1181    
## depthsDeep                0.0214  0.0445   0.4803  0.6310   -0.0658   0.1085    
## depthsVery deep           0.0682  0.0492   1.3849  0.1661   -0.0283   0.1647    
## depthsFull sample         0.1333  0.1842   0.7235  0.4694   -0.2278   0.4944    
## name                     -4.0513  3.1606  -1.2818  0.1999  -10.2460   2.1434    
## depthsSubsurface:name    14.4459  7.1769   2.0128  0.0441    0.3795  28.5123  * 
## depthsDeep:name          11.1463  6.3915   1.7439  0.0812   -1.3809  23.6734  . 
## depthsVery deep:name     13.3985  8.2758   1.6190  0.1054   -2.8219  29.6188    
## depthsFull sample:name    3.5117  4.9772   0.7056  0.4805   -6.2435  13.2669    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0797  0.2823     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.2973, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 25.2282, p-val = 0.0027
## 
## Model Results:
## 
##                            estimate      se     zval    pval     ci.lb    ci.ub 
## intrcpt                     -0.0495  0.0758  -0.6530  0.5138   -0.1982   0.0991 
## depthsSubsurface             0.0244  0.0478   0.5100  0.6101   -0.0693   0.1181 
## depthsDeep                   0.0214  0.0445   0.4803  0.6310   -0.0658   0.1085 
## depthsVery deep              0.0682  0.0492   1.3849  0.1661   -0.0283   0.1647 
## depthsFull sample            0.1333  0.1842   0.7235  0.4694   -0.2278   0.4944 
## nppsurf                     -4.0513  3.1606  -1.2818  0.1999  -10.2460   2.1434 
## depthsSubsurface:nppsurf    14.4459  7.1769   2.0128  0.0441    0.3795  28.5123 
## depthsDeep:nppsurf          11.1463  6.3915   1.7439  0.0812   -1.3809  23.6734 
## depthsVery deep:nppsurf     13.3985  8.2758   1.6190  0.1054   -2.8219  29.6188 
## depthsFull sample:nppsurf    3.5117  4.9772   0.7056  0.4805   -6.2435  13.2669 
##                              
## intrcpt                      
## depthsSubsurface             
## depthsDeep                   
## depthsVery deep              
## depthsFull sample            
## nppsurf                      
## depthsSubsurface:nppsurf   * 
## depthsDeep:nppsurf         . 
## depthsVery deep:nppsurf      
## depthsFull sample:nppsurf    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 181; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0797  0.2823     31     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 171) = 602.2973, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 25.4042, p-val = 0.0046
## 
## Model Results:
## 
##                            estimate      se     zval    pval     ci.lb    ci.ub 
## depthsSurface               -0.0495  0.0758  -0.6530  0.5138   -0.1982   0.0991 
## depthsSubsurface            -0.0251  0.0863  -0.2914  0.7708   -0.1942   0.1439 
## depthsDeep                  -0.0282  0.0838  -0.3361  0.7368   -0.1924   0.1361 
## depthsVery deep              0.0187  0.0870   0.2146  0.8301   -0.1519   0.1893 
## depthsFull sample            0.0838  0.1691   0.4954  0.6203   -0.2477   0.4152 
## nppsurf                     -4.0513  3.1606  -1.2818  0.1999  -10.2460   2.1434 
## depthsSubsurface:nppsurf    14.4459  7.1769   2.0128  0.0441    0.3795  28.5123 
## depthsDeep:nppsurf          11.1463  6.3915   1.7439  0.0812   -1.3809  23.6734 
## depthsVery deep:nppsurf     13.3985  8.2758   1.6190  0.1054   -2.8219  29.6188 
## depthsFull sample:nppsurf    3.5117  4.9772   0.7056  0.4805   -6.2435  13.2669 
##                              
## depthsSurface                
## depthsSubsurface             
## depthsDeep                   
## depthsVery deep              
## depthsFull sample            
## nppsurf                      
## depthsSubsurface:nppsurf   * 
## depthsDeep:nppsurf         . 
## depthsVery deep:nppsurf      
## depthsFull sample:nppsurf    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.7 Season

modTOCsea: significant model fit. modTOCseai: significance only due to 1 combination (subsurface:spring)

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0715  0.2674     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 168) = 562.3719, p-val < .0001
## 
## Test of Moderators (coefficients 2:22):
## QM(df = 21) = 33.7491, p-val = 0.0385
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## intrcpt                           -0.1153  0.0759  -1.5190  0.1288  -0.2642 
## depthsSubsurface                   0.1443  0.0433   3.3336  0.0009   0.0595 
## depthsDeep                         0.1081  0.0413   2.6156  0.0089   0.0271 
## depthsVery deep                    0.1207  0.0444   2.7207  0.0065   0.0337 
## depthsFull sample                  0.1039  0.2312   0.4493  0.6532  -0.3492 
## hseasonDry                         0.0085  0.2837   0.0300  0.9761  -0.5475 
## hseasonSpring                      0.0271  0.0866   0.3136  0.7538  -0.1425 
## hseasonSummer                      0.0812  0.0645   1.2582  0.2083  -0.0453 
## hseasonWet                        -0.0830  0.2850  -0.2914  0.7708  -0.6416 
## hseasonWinter                      0.0999  0.0814   1.2269  0.2199  -0.0597 
## hseasonYearly                     -0.0470  0.2226  -0.2109  0.8330  -0.4833 
## depthsSubsurface:hseasonSpring    -0.1033  0.0749  -1.3781  0.1682  -0.2502 
## depthsDeep:hseasonSpring          -0.0860  0.1060  -0.8116  0.4170  -0.2938 
## depthsVery deep:hseasonSpring      0.0786  0.7074   0.1111  0.9115  -1.3078 
## depthsFull sample:hseasonSpring    0.4084  0.3279   1.2455  0.2129  -0.2343 
## depthsSubsurface:hseasonSummer    -0.1734  0.0790  -2.1932  0.0283  -0.3283 
## depthsDeep:hseasonSummer          -0.1416  0.0824  -1.7179  0.0858  -0.3031 
## depthsVery deep:hseasonSummer     -0.0230  0.0832  -0.2761  0.7824  -0.1859 
## depthsSubsurface:hseasonWinter    -0.1795  0.1245  -1.4416  0.1494  -0.4236 
## depthsSubsurface:hseasonYearly    -0.0287  0.1007  -0.2847  0.7759  -0.2260 
## depthsDeep:hseasonYearly           0.1336  0.1293   1.0332  0.3015  -0.1198 
## depthsVery deep:hseasonYearly      0.1171  0.0973   1.2039  0.2286  -0.0736 
##                                    ci.ub      
## intrcpt                           0.0335      
## depthsSubsurface                  0.2291  *** 
## depthsDeep                        0.1891   ** 
## depthsVery deep                   0.2076   ** 
## depthsFull sample                 0.5570      
## hseasonDry                        0.5645      
## hseasonSpring                     0.1968      
## hseasonSummer                     0.2076      
## hseasonWet                        0.4755      
## hseasonWinter                     0.2594      
## hseasonYearly                     0.3894      
## depthsSubsurface:hseasonSpring    0.0436      
## depthsDeep:hseasonSpring          0.1217      
## depthsVery deep:hseasonSpring     1.4650      
## depthsFull sample:hseasonSpring   1.0511      
## depthsSubsurface:hseasonSummer   -0.0184    * 
## depthsDeep:hseasonSummer          0.0200    . 
## depthsVery deep:hseasonSummer     0.1400      
## depthsSubsurface:hseasonWinter    0.0645      
## depthsSubsurface:hseasonYearly    0.1687      
## depthsDeep:hseasonYearly          0.3869      
## depthsVery deep:hseasonYearly     0.3078      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0715  0.2674     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 168) = 562.3719, p-val < .0001
## 
## Test of Moderators (coefficients 1:22):
## QM(df = 22) = 33.9814, p-val = 0.0493
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## depthsSurface                     -0.1153  0.0759  -1.5190  0.1288  -0.2642 
## depthsSubsurface                   0.0289  0.0809   0.3578  0.7205  -0.1296 
## depthsDeep                        -0.0073  0.0816  -0.0892  0.9289  -0.1672 
## depthsVery deep                    0.0053  0.0833   0.0640  0.9490  -0.1579 
## depthsFull sample                 -0.0115  0.2340  -0.0490  0.9609  -0.4701 
## hseasonDry                         0.0085  0.2837   0.0300  0.9761  -0.5475 
## hseasonSpring                      0.0271  0.0866   0.3136  0.7538  -0.1425 
## hseasonSummer                      0.0812  0.0645   1.2582  0.2083  -0.0453 
## hseasonWet                        -0.0830  0.2850  -0.2914  0.7708  -0.6416 
## hseasonWinter                      0.0999  0.0814   1.2269  0.2199  -0.0597 
## hseasonYearly                     -0.0470  0.2226  -0.2109  0.8330  -0.4833 
## depthsSubsurface:hseasonSpring    -0.1033  0.0749  -1.3781  0.1682  -0.2502 
## depthsDeep:hseasonSpring          -0.0860  0.1060  -0.8116  0.4170  -0.2938 
## depthsVery deep:hseasonSpring      0.0786  0.7074   0.1111  0.9115  -1.3078 
## depthsFull sample:hseasonSpring    0.4084  0.3279   1.2455  0.2129  -0.2343 
## depthsSubsurface:hseasonSummer    -0.1734  0.0790  -2.1932  0.0283  -0.3283 
## depthsDeep:hseasonSummer          -0.1416  0.0824  -1.7179  0.0858  -0.3031 
## depthsVery deep:hseasonSummer     -0.0230  0.0832  -0.2761  0.7824  -0.1859 
## depthsSubsurface:hseasonWinter    -0.1795  0.1245  -1.4416  0.1494  -0.4236 
## depthsSubsurface:hseasonYearly    -0.0287  0.1007  -0.2847  0.7759  -0.2260 
## depthsDeep:hseasonYearly           0.1336  0.1293   1.0332  0.3015  -0.1198 
## depthsVery deep:hseasonYearly      0.1171  0.0973   1.2039  0.2286  -0.0736 
##                                    ci.ub    
## depthsSurface                     0.0335    
## depthsSubsurface                  0.1874    
## depthsDeep                        0.1526    
## depthsVery deep                   0.1685    
## depthsFull sample                 0.4472    
## hseasonDry                        0.5645    
## hseasonSpring                     0.1968    
## hseasonSummer                     0.2076    
## hseasonWet                        0.4755    
## hseasonWinter                     0.2594    
## hseasonYearly                     0.3894    
## depthsSubsurface:hseasonSpring    0.0436    
## depthsDeep:hseasonSpring          0.1217    
## depthsVery deep:hseasonSpring     1.4650    
## depthsFull sample:hseasonSpring   1.0511    
## depthsSubsurface:hseasonSummer   -0.0184  * 
## depthsDeep:hseasonSummer          0.0200  . 
## depthsVery deep:hseasonSummer     0.1400    
## depthsSubsurface:hseasonWinter    0.0645    
## depthsSubsurface:hseasonYearly    0.1687    
## depthsDeep:hseasonYearly          0.3869    
## depthsVery deep:hseasonYearly     0.3078    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.1.8 Habitat

modTOChab: significant model fit modTOChabi: significant regressions for subsurface and full samples.

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0598  0.2445     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 180) = 563.3535, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 27.7872, p-val = 0.0010
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                          -0.0862  0.0688  -1.2526  0.2104  -0.2210 
## depthsSubsurface                  0.0615  0.0302   2.0363  0.0417   0.0023 
## depthsDeep                        0.0622  0.0325   1.9171  0.0552  -0.0014 
## depthsVery deep                   0.1133  0.0332   3.4171  0.0006   0.0483 
## depthsFull sample                 0.4130  0.1630   2.5343  0.0113   0.0936 
## hhabtypeSand                     -0.0952  0.1151  -0.8267  0.4084  -0.3208 
## depthsSubsurface:hhabtypeSand     0.2754  0.1198   2.2988  0.0215   0.0406 
## depthsDeep:hhabtypeSand           0.0668  0.1183   0.5650  0.5721  -0.1650 
## depthsVery deep:hhabtypeSand      0.1384  0.4238   0.3265  0.7440  -0.6923 
## depthsFull sample:hhabtypeSand   -0.4786  0.2451  -1.9524  0.0509  -0.9590 
##                                  ci.ub      
## intrcpt                         0.0487      
## depthsSubsurface                0.1207    * 
## depthsDeep                      0.1258    . 
## depthsVery deep                 0.1783  *** 
## depthsFull sample               0.7324    * 
## hhabtypeSand                    0.1305      
## depthsSubsurface:hhabtypeSand   0.5102    * 
## depthsDeep:hhabtypeSand         0.2986      
## depthsVery deep:hhabtypeSand    0.9690      
## depthsFull sample:hhabtypeSand  0.0019    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 190; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0598  0.2445     33     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 180) = 563.3535, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 28.0469, p-val = 0.0018
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                    -0.0862  0.0688  -1.2526  0.2104  -0.2210 
## depthsSubsurface                 -0.0247  0.0707  -0.3489  0.7271  -0.1633 
## depthsDeep                       -0.0240  0.0712  -0.3367  0.7364  -0.1635 
## depthsVery deep                   0.0272  0.0720   0.3773  0.7059  -0.1139 
## depthsFull sample                 0.3268  0.1477   2.2123  0.0269   0.0373 
## hhabtypeSand                     -0.0952  0.1151  -0.8267  0.4084  -0.3208 
## depthsSubsurface:hhabtypeSand     0.2754  0.1198   2.2988  0.0215   0.0406 
## depthsDeep:hhabtypeSand           0.0668  0.1183   0.5650  0.5721  -0.1650 
## depthsVery deep:hhabtypeSand      0.1384  0.4238   0.3265  0.7440  -0.6923 
## depthsFull sample:hhabtypeSand   -0.4786  0.2451  -1.9524  0.0509  -0.9590 
##                                  ci.ub    
## depthsSurface                   0.0487    
## depthsSubsurface                0.1139    
## depthsDeep                      0.1155    
## depthsVery deep                 0.1682    
## depthsFull sample               0.6164  * 
## hhabtypeSand                    0.1305    
## depthsSubsurface:hhabtypeSand   0.5102  * 
## depthsDeep:hhabtypeSand         0.2986    
## depthsVery deep:hhabtypeSand    0.9690    
## depthsFull sample:hhabtypeSand  0.0019  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2 TN

No significant difference from 0 for the lnRR of TN measurements along depth strata

## 
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0294 (SE = 0.0187)
## tau (square root of estimated tau^2 value):             0.1716
## I^2 (residual heterogeneity / unaccounted variability): 56.63%
## H^2 (unaccounted variability / sampling variability):   2.31
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 43.8520, p-val = 0.0010
## 
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 2.5472, p-val = 0.6362
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt             -0.1399  0.0863  -1.6209  0.1050  -0.3091  0.0293    
## depthsSubsurface     0.1212  0.1263   0.9600  0.3371  -0.1263  0.3687    
## depthsDeep           0.1272  0.1649   0.7714  0.4405  -0.1959  0.4503    
## depthsVery deep      0.1000  0.1922   0.5203  0.6028  -0.2766  0.4766    
## depthsFull sample    0.2350  0.1541   1.5251  0.1272  -0.0670  0.5371    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0294 (SE = 0.0187)
## tau (square root of estimated tau^2 value):             0.1716
## I^2 (residual heterogeneity / unaccounted variability): 56.63%
## H^2 (unaccounted variability / sampling variability):   2.31
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 43.8520, p-val = 0.0010
## 
## Test of Moderators (coefficients 1:5):
## QM(df = 5) = 3.2860, p-val = 0.6560
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub    
## depthsSurface       -0.1399  0.0863  -1.6209  0.1050  -0.3091  0.0293    
## depthsSubsurface    -0.0187  0.0922  -0.2029  0.8392  -0.1993  0.1619    
## depthsDeep          -0.0128  0.1404  -0.0908  0.9276  -0.2880  0.2625    
## depthsVery deep     -0.0399  0.1717  -0.2326  0.8161  -0.3764  0.2966    
## depthsFull sample    0.0951  0.1277   0.7450  0.4563  -0.1551  0.3453    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.1 Time since trawling

–> No significant relations with time-since trawling.

## 
## Multivariate Meta-Analysis Model (k = 38; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0211  0.1453     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 32) = 51.7639, p-val = 0.0150
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 6.5387, p-val = 0.2573
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  -0.0437  0.1172  -0.3727  0.7094  -0.2734  0.1860    
## depthsSubsurface         -0.0208  0.1914  -0.1086  0.9135  -0.3960  0.3544    
## depthsFull sample         0.1466  0.1848   0.7931  0.4277  -0.2157  0.5088    
## name                     -0.0693  0.0934  -0.7417  0.4583  -0.2523  0.1138    
## depthsSubsurface:name     0.1060  0.1135   0.9340  0.3503  -0.1164  0.3284    
## depthsFull sample:name    0.0338  0.1916   0.1766  0.8598  -0.3417  0.4093    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 38; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0211  0.1453     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 32) = 51.7639, p-val = 0.0150
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 6.5387, p-val = 0.2573
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     -0.0437  0.1172  -0.3727  0.7094 
## depthsSubsurface                            -0.0208  0.1914  -0.1086  0.9135 
## depthsFull sample                            0.1466  0.1848   0.7931  0.4277 
## log(timesincetrawl + 1)                     -0.0693  0.0934  -0.7417  0.4583 
## depthsSubsurface:log(timesincetrawl + 1)     0.1060  0.1135   0.9340  0.3503 
## depthsFull sample:log(timesincetrawl + 1)    0.0338  0.1916   0.1766  0.8598 
##                                              ci.lb   ci.ub    
## intrcpt                                    -0.2734  0.1860    
## depthsSubsurface                           -0.3960  0.3544    
## depthsFull sample                          -0.2157  0.5088    
## log(timesincetrawl + 1)                    -0.2523  0.1138    
## depthsSubsurface:log(timesincetrawl + 1)   -0.1164  0.3284    
## depthsFull sample:log(timesincetrawl + 1)  -0.3417  0.4093    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 38; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0211  0.1453     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 32) = 51.7639, p-val = 0.0150
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 6.5643, p-val = 0.3630
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## depthsSurface                               -0.0437  0.1172  -0.3727  0.7094 
## depthsSubsurface                            -0.0645  0.1608  -0.4010  0.6884 
## depthsFull sample                            0.1029  0.1502   0.6853  0.4931 
## log(timesincetrawl + 1)                     -0.0693  0.0934  -0.7417  0.4583 
## depthsSubsurface:log(timesincetrawl + 1)     0.1060  0.1135   0.9340  0.3503 
## depthsFull sample:log(timesincetrawl + 1)    0.0338  0.1916   0.1766  0.8598 
##                                              ci.lb   ci.ub    
## depthsSurface                              -0.2734  0.1860    
## depthsSubsurface                           -0.3796  0.2507    
## depthsFull sample                          -0.1914  0.3972    
## log(timesincetrawl + 1)                    -0.2523  0.1138    
## depthsSubsurface:log(timesincetrawl + 1)   -0.1164  0.3284    
## depthsFull sample:log(timesincetrawl + 1)  -0.3417  0.4093    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.2 Trawling frequency

–> No significant relation with trawling frequency.

## 
## Multivariate Meta-Analysis Model (k = 34; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0094  0.0968      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 22.0596, p-val = 0.7785
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 8.6447, p-val = 0.1241
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  -0.0776  0.0719  -1.0790  0.2806  -0.2185  0.0633    
## depthsSubsurface          0.1547  0.0707   2.1876  0.0287   0.0161  0.2933  * 
## depthsFull sample         0.4755  1.1704   0.4063  0.6845  -1.8184  2.7694    
## name                     -0.0018  0.0014  -1.2441  0.2135  -0.0046  0.0010    
## depthsSubsurface:name    -0.0012  0.0012  -0.9871  0.3236  -0.0035  0.0011    
## depthsFull sample:name   -0.0401  0.1777  -0.2255  0.8216  -0.3883  0.3082    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 45; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0094  0.0968      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 28.6322, p-val = 0.8361
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 16.0994, p-val = 0.0242
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## intrcpt                        -0.0776  0.0719  -1.0790  0.2806  -0.2185 
## depthsSubsurface                0.1547  0.0707   2.1876  0.0287   0.0161 
## depthsDeep                     -0.0266  0.0836  -0.3178  0.7506  -0.1905 
## depthsVery deep                 0.1382  0.0816   1.6934  0.0904  -0.0218 
## depthsFull sample               0.4755  1.1704   0.4063  0.6845  -1.8184 
## heffortnum                     -0.0018  0.0014  -1.2441  0.2135  -0.0046 
## depthsSubsurface:heffortnum    -0.0012  0.0012  -0.9871  0.3236  -0.0035 
## depthsFull sample:heffortnum   -0.0401  0.1777  -0.2255  0.8216  -0.3883 
##                                ci.ub    
## intrcpt                       0.0633    
## depthsSubsurface              0.2933  * 
## depthsDeep                    0.1374    
## depthsVery deep               0.2982  . 
## depthsFull sample             2.7694    
## heffortnum                    0.0010    
## depthsSubsurface:heffortnum   0.0011    
## depthsFull sample:heffortnum  0.3082    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 45; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0094  0.0968      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 28.6322, p-val = 0.8361
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 17.6867, p-val = 0.0237
## 
## Model Results:
## 
##                               estimate      se     zval    pval    ci.lb 
## depthsSurface                  -0.0776  0.0719  -1.0790  0.2806  -0.2185 
## depthsSubsurface                0.0771  0.0776   0.9943  0.3201  -0.0749 
## depthsDeep                     -0.1042  0.1118  -0.9316  0.3516  -0.3233 
## depthsVery deep                 0.0606  0.1103   0.5497  0.5825  -0.1556 
## depthsFull sample               0.3979  1.1694   0.3403  0.7336  -1.8940 
## heffortnum                     -0.0018  0.0014  -1.2441  0.2135  -0.0046 
## depthsSubsurface:heffortnum    -0.0012  0.0012  -0.9871  0.3236  -0.0035 
## depthsFull sample:heffortnum   -0.0401  0.1777  -0.2255  0.8216  -0.3883 
##                                ci.ub    
## depthsSurface                 0.0633    
## depthsSubsurface              0.2292    
## depthsDeep                    0.1150    
## depthsVery deep               0.2769    
## depthsFull sample             2.6898    
## heffortnum                    0.0010    
## depthsSubsurface:heffortnum   0.0011    
## depthsFull sample:heffortnum  0.3082    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.3 Current velocity

–> Significant relations, lnRR always more negative in low current setting.

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 40.7210, p-val = 0.9921
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 107.9409, p-val < .0001
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                  -0.2803   0.0780  -3.5940  0.0003   -0.4331  -0.1274 
## depthsSubsurface         -0.2494   0.1347  -1.8515  0.0641   -0.5133   0.0146 
## depthsDeep               -0.9334   0.2163  -4.3154  <.0001   -1.3573  -0.5095 
## depthsVery deep          -0.4252   0.5011  -0.8485  0.3962   -1.4074   0.5570 
## depthsFull sample        -0.4825   0.1798  -2.6833  0.0073   -0.8349  -0.1301 
## name                      1.6762   0.9486   1.7671  0.0772   -0.1830   3.5354 
## depthsSubsurface:name     4.9329   1.8941   2.6044  0.0092    1.2205   8.6452 
## depthsDeep:name          20.0082   4.3319   4.6188  <.0001   11.5178  28.4985 
## depthsVery deep:name     11.9130  11.5365   1.0326  0.3018  -10.6983  34.5242 
## depthsFull sample:name    2.2929   1.2115   1.8925  0.0584   -0.0817   4.6674 
##                             
## intrcpt                 *** 
## depthsSubsurface          . 
## depthsDeep              *** 
## depthsVery deep             
## depthsFull sample        ** 
## name                      . 
## depthsSubsurface:name    ** 
## depthsDeep:name         *** 
## depthsVery deep:name        
## depthsFull sample:name    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 40.7210, p-val = 0.9921
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 107.9409, p-val < .0001
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                  -0.2803   0.0780  -3.5940  0.0003   -0.4331  -0.1274 
## depthsSubsurface         -0.2494   0.1347  -1.8515  0.0641   -0.5133   0.0146 
## depthsDeep               -0.9334   0.2163  -4.3154  <.0001   -1.3573  -0.5095 
## depthsVery deep          -0.4252   0.5011  -0.8485  0.3962   -1.4074   0.5570 
## depthsFull sample        -0.4825   0.1798  -2.6833  0.0073   -0.8349  -0.1301 
## cvel                      1.6762   0.9486   1.7671  0.0772   -0.1830   3.5354 
## depthsSubsurface:cvel     4.9329   1.8941   2.6044  0.0092    1.2205   8.6452 
## depthsDeep:cvel          20.0082   4.3319   4.6188  <.0001   11.5178  28.4985 
## depthsVery deep:cvel     11.9130  11.5365   1.0326  0.3018  -10.6983  34.5242 
## depthsFull sample:cvel    2.2929   1.2115   1.8925  0.0584   -0.0817   4.6674 
##                             
## intrcpt                 *** 
## depthsSubsurface          . 
## depthsDeep              *** 
## depthsVery deep             
## depthsFull sample        ** 
## cvel                      . 
## depthsSubsurface:cvel    ** 
## depthsDeep:cvel         *** 
## depthsVery deep:cvel        
## depthsFull sample:cvel    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 40.7210, p-val = 0.9921
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 147.7345, p-val < .0001
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## depthsSurface            -0.2803   0.0780  -3.5940  0.0003   -0.4331  -0.1274 
## depthsSubsurface         -0.5296   0.1098  -4.8232  <.0001   -0.7448  -0.3144 
## depthsDeep               -1.2136   0.2017  -6.0157  <.0001   -1.6091  -0.8182 
## depthsVery deep          -0.7054   0.4950  -1.4251  0.1541   -1.6757   0.2648 
## depthsFull sample        -0.7627   0.1620  -4.7078  <.0001   -1.0803  -0.4452 
## cvel                      1.6762   0.9486   1.7671  0.0772   -0.1830   3.5354 
## depthsSubsurface:cvel     4.9329   1.8941   2.6044  0.0092    1.2205   8.6452 
## depthsDeep:cvel          20.0082   4.3319   4.6188  <.0001   11.5178  28.4985 
## depthsVery deep:cvel     11.9130  11.5365   1.0326  0.3018  -10.6983  34.5242 
## depthsFull sample:cvel    2.2929   1.2115   1.8925  0.0584   -0.0817   4.6674 
##                             
## depthsSurface           *** 
## depthsSubsurface        *** 
## depthsDeep              *** 
## depthsVery deep             
## depthsFull sample       *** 
## cvel                      . 
## depthsSubsurface:cvel    ** 
## depthsDeep:cvel         *** 
## depthsVery deep:cvel        
## depthsFull sample:cvel    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.4 Water depth

-> No significant relations of TN-lnRR with water depth.

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0215  0.1465     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 66) = 78.3219, p-val = 0.1424
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 14.3656, p-val = 0.1099
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  -0.0145  0.0801  -0.1814  0.8561  -0.1716  0.1425    
## depthsSubsurface          0.0263  0.0770   0.3416  0.7326  -0.1247  0.1773    
## depthsDeep                0.1823  0.1759   1.0360  0.3002  -0.1625  0.5271    
## depthsVery deep           0.1341  0.3322   0.4036  0.6865  -0.5171  0.7852    
## depthsFull sample         0.1875  0.4536   0.4133  0.6794  -0.7016  1.0765    
## name                     -0.0005  0.0003  -1.5686  0.1167  -0.0011  0.0001    
## depthsSubsurface:name     0.0001  0.0002   0.5531  0.5802  -0.0003  0.0006    
## depthsDeep:name          -0.0003  0.0004  -0.8760  0.3810  -0.0010  0.0004    
## depthsVery deep:name      0.0001  0.0006   0.1177  0.9063  -0.0011  0.0013    
## depthsFull sample:name   -0.0046  0.0243  -0.1893  0.8498  -0.0522  0.0430    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0215  0.1465     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 66) = 78.3219, p-val = 0.1424
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 14.3656, p-val = 0.1099
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                      -0.0145  0.0801  -0.1814  0.8561  -0.1716  0.1425 
## depthsSubsurface              0.0263  0.0770   0.3416  0.7326  -0.1247  0.1773 
## depthsDeep                    0.1823  0.1759   1.0360  0.3002  -0.1625  0.5271 
## depthsVery deep               0.1341  0.3322   0.4036  0.6865  -0.5171  0.7852 
## depthsFull sample             0.1875  0.4536   0.4133  0.6794  -0.7016  1.0765 
## watdepth                     -0.0005  0.0003  -1.5686  0.1167  -0.0011  0.0001 
## depthsSubsurface:watdepth     0.0001  0.0002   0.5531  0.5802  -0.0003  0.0006 
## depthsDeep:watdepth          -0.0003  0.0004  -0.8760  0.3810  -0.0010  0.0004 
## depthsVery deep:watdepth      0.0001  0.0006   0.1177  0.9063  -0.0011  0.0013 
## depthsFull sample:watdepth   -0.0046  0.0243  -0.1893  0.8498  -0.0522  0.0430 
##                               
## intrcpt                       
## depthsSubsurface              
## depthsDeep                    
## depthsVery deep               
## depthsFull sample             
## watdepth                      
## depthsSubsurface:watdepth     
## depthsDeep:watdepth           
## depthsVery deep:watdepth      
## depthsFull sample:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0215  0.1465     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 66) = 78.3219, p-val = 0.1424
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 14.4633, p-val = 0.1529
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb   ci.ub 
## depthsSurface                -0.0145  0.0801  -0.1814  0.8561  -0.1716  0.1425 
## depthsSubsurface              0.0118  0.0866   0.1361  0.8917  -0.1579  0.1815 
## depthsDeep                    0.1677  0.1637   1.0245  0.3056  -0.1532  0.4886 
## depthsVery deep               0.1195  0.3259   0.3668  0.7138  -0.5193  0.7583 
## depthsFull sample             0.1729  0.4513   0.3831  0.7016  -0.7117  1.0575 
## watdepth                     -0.0005  0.0003  -1.5686  0.1167  -0.0011  0.0001 
## depthsSubsurface:watdepth     0.0001  0.0002   0.5531  0.5802  -0.0003  0.0006 
## depthsDeep:watdepth          -0.0003  0.0004  -0.8760  0.3810  -0.0010  0.0004 
## depthsVery deep:watdepth      0.0001  0.0006   0.1177  0.9063  -0.0011  0.0013 
## depthsFull sample:watdepth   -0.0046  0.0243  -0.1893  0.8498  -0.0522  0.0430 
##                               
## depthsSurface                 
## depthsSubsurface              
## depthsDeep                    
## depthsVery deep               
## depthsFull sample             
## watdepth                      
## depthsSubsurface:watdepth     
## depthsDeep:watdepth           
## depthsVery deep:watdepth      
## depthsFull sample:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.5 Primary productivity

–> Significant models, lnRR for TN more negative in areas with low surface NPP.

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 39.1917, p-val = 0.9953
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 109.4702, p-val < .0001
## 
## Model Results:
## 
##                         estimate        se     zval    pval      ci.lb 
## intrcpt                  -0.2062    0.0471  -4.3803  <.0001    -0.2985 
## depthsSubsurface         -0.0531    0.0686  -0.7742  0.4388    -0.1874 
## depthsDeep               -0.2287    0.0773  -2.9588  0.0031    -0.3803 
## depthsVery deep          -0.0116    0.1016  -0.1138  0.9094    -0.2106 
## depthsFull sample         0.0045    0.0762   0.0589  0.9530    -0.1449 
## name                     11.6425    7.8059   1.4915  0.1358    -3.6567 
## depthsSubsurface:name    32.1558   13.6781   2.3509  0.0187     5.3471 
## depthsDeep:name         212.9229   44.4318   4.7921  <.0001   125.8383 
## depthsVery deep:name    129.6184  119.2136   1.0873  0.2769  -104.0360 
## depthsFull sample:name   -6.6181    7.8576  -0.8423  0.3996   -22.0186 
##                            ci.ub      
## intrcpt                  -0.1139  *** 
## depthsSubsurface          0.0813      
## depthsDeep               -0.0772   ** 
## depthsVery deep           0.1875      
## depthsFull sample         0.1539      
## name                     26.9418      
## depthsSubsurface:name    58.9644    * 
## depthsDeep:name         300.0075  *** 
## depthsVery deep:name    363.2728      
## depthsFull sample:name    8.7825      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 39.1917, p-val = 0.9953
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 109.4702, p-val < .0001
## 
## Model Results:
## 
##                            estimate        se     zval    pval      ci.lb 
## intrcpt                     -0.2062    0.0471  -4.3803  <.0001    -0.2985 
## depthsSubsurface            -0.0531    0.0686  -0.7742  0.4388    -0.1874 
## depthsDeep                  -0.2287    0.0773  -2.9588  0.0031    -0.3803 
## depthsVery deep             -0.0116    0.1016  -0.1138  0.9094    -0.2106 
## depthsFull sample            0.0045    0.0762   0.0589  0.9530    -0.1449 
## nppsurf                     11.6425    7.8059   1.4915  0.1358    -3.6567 
## depthsSubsurface:nppsurf    32.1558   13.6781   2.3509  0.0187     5.3471 
## depthsDeep:nppsurf         212.9229   44.4318   4.7921  <.0001   125.8383 
## depthsVery deep:nppsurf    129.6184  119.2136   1.0873  0.2769  -104.0360 
## depthsFull sample:nppsurf   -6.6181    7.8576  -0.8423  0.3996   -22.0186 
##                               ci.ub      
## intrcpt                     -0.1139  *** 
## depthsSubsurface             0.0813      
## depthsDeep                  -0.0772   ** 
## depthsVery deep              0.1875      
## depthsFull sample            0.1539      
## nppsurf                     26.9418      
## depthsSubsurface:nppsurf    58.9644    * 
## depthsDeep:nppsurf         300.0075  *** 
## depthsVery deep:nppsurf    363.2728      
## depthsFull sample:nppsurf    8.7825      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 75; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 65) = 39.1917, p-val = 0.9953
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 149.2638, p-val < .0001
## 
## Model Results:
## 
##                            estimate        se     zval    pval      ci.lb 
## depthsSurface               -0.2062    0.0471  -4.3803  <.0001    -0.2985 
## depthsSubsurface            -0.2593    0.0498  -5.2033  <.0001    -0.3570 
## depthsDeep                  -0.4350    0.0613  -7.0930  <.0001    -0.5552 
## depthsVery deep             -0.2178    0.0900  -2.4202  0.0155    -0.3941 
## depthsFull sample           -0.2017    0.0600  -3.3639  0.0008    -0.3193 
## nppsurf                     11.6425    7.8059   1.4915  0.1358    -3.6567 
## depthsSubsurface:nppsurf    32.1558   13.6781   2.3509  0.0187     5.3471 
## depthsDeep:nppsurf         212.9229   44.4318   4.7921  <.0001   125.8383 
## depthsVery deep:nppsurf    129.6184  119.2136   1.0873  0.2769  -104.0360 
## depthsFull sample:nppsurf   -6.6181    7.8576  -0.8423  0.3996   -22.0186 
##                               ci.ub      
## depthsSurface               -0.1139  *** 
## depthsSubsurface            -0.1616  *** 
## depthsDeep                  -0.3148  *** 
## depthsVery deep             -0.0414    * 
## depthsFull sample           -0.0842  *** 
## nppsurf                     26.9418      
## depthsSubsurface:nppsurf    58.9644    * 
## depthsDeep:nppsurf         300.0075  *** 
## depthsVery deep:nppsurf    363.2728      
## depthsFull sample:nppsurf    8.7825      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.6 Season

–> No significant effects of season.

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0373  0.1932     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 61) = 98.8914, p-val = 0.0015
## 
## Test of Moderators (coefficients 2:15):
## QM(df = 14) = 14.7773, p-val = 0.3935
## 
## Model Results:
## 
##                                  estimate       se     zval    pval     ci.lb 
## intrcpt                           -0.1694   0.2457  -0.6894  0.4906   -0.6510 
## depthsSubsurface                   3.3108   9.3260   0.3550  0.7226  -14.9678 
## depthsDeep                         3.4399   9.2173   0.3732  0.7090  -14.6257 
## depthsVery deep                    3.4733  12.4244   0.2796  0.7798  -20.8780 
## depthsFull sample                  0.1727   0.2537   0.6804  0.4962   -0.3247 
## hseasonDry                         0.0893   0.3814   0.2343  0.8148   -0.6582 
## hseasonSpring                     -0.0144   0.2955  -0.0487  0.9612   -0.5936 
## hseasonSummer                      0.1382   0.2625   0.5263  0.5987   -0.3764 
## hseasonWet                         0.0259   0.3833   0.0675  0.9462   -0.7253 
## hseasonYearly                     -2.9408   9.3255  -0.3153  0.7525  -21.2184 
## depthsSubsurface:hseasonSpring    -3.1647   9.3263  -0.3393  0.7344  -21.4439 
## depthsFull sample:hseasonSpring   -0.3288   1.4329  -0.2295  0.8185   -3.1373 
## depthsSubsurface:hseasonSummer    -3.3349   9.3262  -0.3576  0.7207  -21.6139 
## depthsDeep:hseasonSummer          -3.4902   9.2176  -0.3786  0.7050  -21.5564 
## depthsVery deep:hseasonSummer     -3.3739  12.4246  -0.2715  0.7860  -27.7256 
##                                    ci.ub    
## intrcpt                           0.3122    
## depthsSubsurface                 21.5894    
## depthsDeep                       21.5056    
## depthsVery deep                  27.8246    
## depthsFull sample                 0.6700    
## hseasonDry                        0.8369    
## hseasonSpring                     0.5649    
## hseasonSummer                     0.6527    
## hseasonWet                        0.7770    
## hseasonYearly                    15.3369    
## depthsSubsurface:hseasonSpring   15.1146    
## depthsFull sample:hseasonSpring   2.4796    
## depthsSubsurface:hseasonSummer   14.9442    
## depthsDeep:hseasonSummer         14.5760    
## depthsVery deep:hseasonSummer    20.9778    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0373  0.1932     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 61) = 98.8914, p-val = 0.0015
## 
## Test of Moderators (coefficients 1:15):
## QM(df = 15) = 14.8159, p-val = 0.4648
## 
## Model Results:
## 
##                                  estimate       se     zval    pval     ci.lb 
## depthsSurface                     -0.1694   0.2457  -0.6894  0.4906   -0.6510 
## depthsSubsurface                   3.1414   9.3229   0.3370  0.7362  -15.1312 
## depthsDeep                         3.2706   9.2142   0.3549  0.7226  -14.7890 
## depthsVery deep                    3.3039  12.4221   0.2660  0.7903  -21.0429 
## depthsFull sample                  0.0033   0.3532   0.0093  0.9926   -0.6890 
## hseasonDry                         0.0893   0.3814   0.2343  0.8148   -0.6582 
## hseasonSpring                     -0.0144   0.2955  -0.0487  0.9612   -0.5936 
## hseasonSummer                      0.1382   0.2625   0.5263  0.5987   -0.3764 
## hseasonWet                         0.0259   0.3833   0.0675  0.9462   -0.7253 
## hseasonYearly                     -2.9408   9.3255  -0.3153  0.7525  -21.2184 
## depthsSubsurface:hseasonSpring    -3.1647   9.3263  -0.3393  0.7344  -21.4439 
## depthsFull sample:hseasonSpring   -0.3288   1.4329  -0.2295  0.8185   -3.1373 
## depthsSubsurface:hseasonSummer    -3.3349   9.3262  -0.3576  0.7207  -21.6139 
## depthsDeep:hseasonSummer          -3.4902   9.2176  -0.3786  0.7050  -21.5564 
## depthsVery deep:hseasonSummer     -3.3739  12.4246  -0.2715  0.7860  -27.7256 
##                                    ci.ub    
## depthsSurface                     0.3122    
## depthsSubsurface                 21.4141    
## depthsDeep                       21.3301    
## depthsVery deep                  27.6507    
## depthsFull sample                 0.6955    
## hseasonDry                        0.8369    
## hseasonSpring                     0.5649    
## hseasonSummer                     0.6527    
## hseasonWet                        0.7770    
## hseasonYearly                    15.3369    
## depthsSubsurface:hseasonSpring   15.1146    
## depthsFull sample:hseasonSpring   2.4796    
## depthsSubsurface:hseasonSummer   14.9442    
## depthsDeep:hseasonSummer         14.5760    
## depthsVery deep:hseasonSummer    20.9778    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.2.7 Habitat

–> No significant of habitat, not all combinations present in >= 3 studies.

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0291  0.1706     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 68) = 79.6266, p-val = 0.1583
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 13.9284, p-val = 0.0525
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                          -0.1236  0.0946  -1.3068  0.1913  -0.3089 
## depthsSubsurface                  0.1077  0.0574   1.8787  0.0603  -0.0047 
## depthsDeep                        0.0351  0.0687   0.5112  0.6092  -0.0996 
## depthsVery deep                   0.1813  0.0680   2.6676  0.0076   0.0481 
## depthsFull sample                 0.3924  0.2014   1.9487  0.0513  -0.0023 
## hhabtypeSand                      0.1267  0.1352   0.9371  0.3487  -0.1383 
## depthsSubsurface:hhabtypeSand    -0.1639  0.1008  -1.6260  0.1039  -0.3614 
## depthsFull sample:hhabtypeSand   -0.4306  0.2620  -1.6435  0.1003  -0.9440 
##                                  ci.ub     
## intrcpt                         0.0618     
## depthsSubsurface                0.2202   . 
## depthsDeep                      0.1699     
## depthsVery deep                 0.3145  ** 
## depthsFull sample               0.7871   . 
## hhabtypeSand                    0.3918     
## depthsSubsurface:hhabtypeSand   0.0337     
## depthsFull sample:hhabtypeSand  0.0829     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 76; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0291  0.1706     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 68) = 79.6266, p-val = 0.1583
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 13.9866, p-val = 0.0821
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                    -0.1236  0.0946  -1.3068  0.1913  -0.3089 
## depthsSubsurface                 -0.0158  0.0964  -0.1641  0.8696  -0.2048 
## depthsDeep                       -0.0884  0.0986  -0.8968  0.3698  -0.2817 
## depthsVery deep                   0.0577  0.1006   0.5743  0.5658  -0.1393 
## depthsFull sample                 0.2689  0.1778   1.5121  0.1305  -0.0796 
## hhabtypeSand                      0.1267  0.1352   0.9371  0.3487  -0.1383 
## depthsSubsurface:hhabtypeSand    -0.1639  0.1008  -1.6260  0.1039  -0.3614 
## depthsFull sample:hhabtypeSand   -0.4306  0.2620  -1.6435  0.1003  -0.9440 
##                                  ci.ub    
## depthsSurface                   0.0618    
## depthsSubsurface                0.1731    
## depthsDeep                      0.1048    
## depthsVery deep                 0.2548    
## depthsFull sample               0.6173    
## hhabtypeSand                    0.3918    
## depthsSubsurface:hhabtypeSand   0.0337    
## depthsFull sample:hhabtypeSand  0.0829    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3 TOC/TN

-> No significant differences along depth strata for lnRR of TOC/TN ratio.

## 
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0033 (SE = 0.0050)
## tau (square root of estimated tau^2 value):             0.0570
## I^2 (residual heterogeneity / unaccounted variability): 19.72%
## H^2 (unaccounted variability / sampling variability):   1.25
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 19.6709, p-val = 0.4146
## 
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 0.9232, p-val = 0.9212
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt              0.0064  0.0403   0.1591  0.8736  -0.0725  0.0853    
## depthsSubsurface    -0.0599  0.0693  -0.8642  0.3875  -0.1957  0.0759    
## depthsDeep          -0.0019  0.0902  -0.0214  0.9829  -0.1788  0.1749    
## depthsVery deep     -0.0091  0.1200  -0.0759  0.9395  -0.2444  0.2261    
## depthsFull sample   -0.0401  0.0702  -0.5705  0.5683  -0.1778  0.0976    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 24; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0033 (SE = 0.0050)
## tau (square root of estimated tau^2 value):             0.0570
## I^2 (residual heterogeneity / unaccounted variability): 19.72%
## H^2 (unaccounted variability / sampling variability):   1.25
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 19.6709, p-val = 0.4146
## 
## Test of Moderators (coefficients 1:5):
## QM(df = 5) = 1.2705, p-val = 0.9379
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub    
## depthsSurface        0.0064  0.0403   0.1591  0.8736  -0.0725  0.0853    
## depthsSubsurface    -0.0535  0.0564  -0.9483  0.3430  -0.1640  0.0570    
## depthsDeep           0.0045  0.0807   0.0555  0.9558  -0.1538  0.1627    
## depthsVery deep     -0.0027  0.1131  -0.0239  0.9809  -0.2243  0.2189    
## depthsFull sample   -0.0337  0.0576  -0.5849  0.5586  -0.1465  0.0791    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.1 Time since trawling

-> No significant relation with time since trawling

## 
## Multivariate Meta-Analysis Model (k = 40; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0053  0.0730     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 34) = 40.2896, p-val = 0.2119
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 4.4791, p-val = 0.4827
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  -0.0286  0.0483  -0.5913  0.5543  -0.1233  0.0661    
## depthsSubsurface         -0.0536  0.0750  -0.7155  0.4743  -0.2006  0.0933    
## depthsFull sample         0.0804  0.0720   1.1171  0.2640  -0.0607  0.2215    
## name                      0.0091  0.0201   0.4539  0.6499  -0.0302  0.0485    
## depthsSubsurface:name    -0.0014  0.0337  -0.0402  0.9680  -0.0673  0.0646    
## depthsFull sample:name   -0.2169  0.1140  -1.9021  0.0572  -0.4404  0.0066  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0053  0.0730     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 40.3030, p-val = 0.2472
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 4.5269, p-val = 0.7175
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     -0.0286  0.0483  -0.5913  0.5543 
## depthsSubsurface                            -0.0536  0.0750  -0.7155  0.4743 
## depthsDeep                                   0.0156  0.1214   0.1283  0.8979 
## depthsFull sample                            0.0804  0.0720   1.1171  0.2640 
## log(timesincetrawl + 1)                      0.0091  0.0201   0.4539  0.6499 
## depthsSubsurface:log(timesincetrawl + 1)    -0.0014  0.0337  -0.0402  0.9680 
## depthsDeep:log(timesincetrawl + 1)          -0.0132  0.0320  -0.4119  0.6804 
## depthsFull sample:log(timesincetrawl + 1)   -0.2169  0.1140  -1.9021  0.0572 
##                                              ci.lb   ci.ub    
## intrcpt                                    -0.1233  0.0661    
## depthsSubsurface                           -0.2006  0.0933    
## depthsDeep                                 -0.2223  0.2534    
## depthsFull sample                          -0.0607  0.2215    
## log(timesincetrawl + 1)                    -0.0302  0.0485    
## depthsSubsurface:log(timesincetrawl + 1)   -0.0673  0.0646    
## depthsDeep:log(timesincetrawl + 1)         -0.0760  0.0496    
## depthsFull sample:log(timesincetrawl + 1)  -0.4404  0.0066  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0053  0.0730     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 40.3030, p-val = 0.2472
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 6.4901, p-val = 0.5925
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## depthsSurface                               -0.0286  0.0483  -0.5913  0.5543 
## depthsSubsurface                            -0.0822  0.0740  -1.1111  0.2665 
## depthsDeep                                  -0.0130  0.1159  -0.1122  0.9107 
## depthsFull sample                            0.0518  0.0682   0.7600  0.4472 
## log(timesincetrawl + 1)                      0.0091  0.0201   0.4539  0.6499 
## depthsSubsurface:log(timesincetrawl + 1)    -0.0014  0.0337  -0.0402  0.9680 
## depthsDeep:log(timesincetrawl + 1)          -0.0132  0.0320  -0.4119  0.6804 
## depthsFull sample:log(timesincetrawl + 1)   -0.2169  0.1140  -1.9021  0.0572 
##                                              ci.lb   ci.ub    
## depthsSurface                              -0.1233  0.0661    
## depthsSubsurface                           -0.2272  0.0628    
## depthsDeep                                 -0.2401  0.2141    
## depthsFull sample                          -0.0819  0.1856    
## log(timesincetrawl + 1)                    -0.0302  0.0485    
## depthsSubsurface:log(timesincetrawl + 1)   -0.0673  0.0646    
## depthsDeep:log(timesincetrawl + 1)         -0.0760  0.0496    
## depthsFull sample:log(timesincetrawl + 1)  -0.4404  0.0066  . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.2 Trawling frequency

-> No signficant relation with trawling frequency.

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0035  0.0589      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 27) = 19.1232, p-val = 0.8655
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 3.7118, p-val = 0.8123
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## intrcpt                                     -0.0287  0.0452  -0.6354  0.5252 
## depthsSubsurface                            -0.0606  0.0722  -0.8391  0.4014 
## depthsDeep                                   0.0175  0.1181   0.1484  0.8820 
## depthsFull sample                            0.0826  0.0739   1.1179  0.2636 
## log(timesincetrawl + 1)                      0.0092  0.0194   0.4769  0.6334 
## depthsSubsurface:log(timesincetrawl + 1)     0.0002  0.0319   0.0055  0.9956 
## depthsDeep:log(timesincetrawl + 1)          -0.0133  0.0316  -0.4217  0.6733 
## depthsFull sample:log(timesincetrawl + 1)   -0.1743  0.1686  -1.0336  0.3013 
##                                              ci.lb   ci.ub    
## intrcpt                                    -0.1174  0.0599    
## depthsSubsurface                           -0.2021  0.0809    
## depthsDeep                                 -0.2140  0.2490    
## depthsFull sample                          -0.0622  0.2274    
## log(timesincetrawl + 1)                    -0.0288  0.0472    
## depthsSubsurface:log(timesincetrawl + 1)   -0.0623  0.0626    
## depthsDeep:log(timesincetrawl + 1)         -0.0752  0.0486    
## depthsFull sample:log(timesincetrawl + 1)  -0.5048  0.1562    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0035  0.0589      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 27) = 19.1232, p-val = 0.8655
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 4.5123, p-val = 0.8082
## 
## Model Results:
## 
##                                            estimate      se     zval    pval 
## depthsSurface                               -0.0287  0.0452  -0.6354  0.5252 
## depthsSubsurface                            -0.0893  0.0680  -1.3130  0.1892 
## depthsDeep                                  -0.0112  0.1118  -0.1002  0.9202 
## depthsFull sample                            0.0539  0.0697   0.7723  0.4400 
## log(timesincetrawl + 1)                      0.0092  0.0194   0.4769  0.6334 
## depthsSubsurface:log(timesincetrawl + 1)     0.0002  0.0319   0.0055  0.9956 
## depthsDeep:log(timesincetrawl + 1)          -0.0133  0.0316  -0.4217  0.6733 
## depthsFull sample:log(timesincetrawl + 1)   -0.1743  0.1686  -1.0336  0.3013 
##                                              ci.lb   ci.ub    
## depthsSurface                              -0.1174  0.0599    
## depthsSubsurface                           -0.2226  0.0440    
## depthsDeep                                 -0.2304  0.2080    
## depthsFull sample                          -0.0828  0.1906    
## log(timesincetrawl + 1)                    -0.0288  0.0472    
## depthsSubsurface:log(timesincetrawl + 1)   -0.0623  0.0626    
## depthsDeep:log(timesincetrawl + 1)         -0.0752  0.0486    
## depthsFull sample:log(timesincetrawl + 1)  -0.5048  0.1562    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.3 Current velocity

-> No significant relation with TOC/TN lnrr and current velocity.

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0194  0.1394     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 107.4958, p-val = 0.0003
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 5.1053, p-val = 0.6471
## 
## Model Results:
## 
##                         estimate        se     zval    pval      ci.lb 
## intrcpt                   0.2174    0.1206   1.8018  0.0716    -0.0191 
## depthsSubsurface         -0.1084    0.1675  -0.6475  0.5173    -0.4367 
## depthsDeep                2.8219    4.5559   0.6194  0.5357    -6.1074 
## depthsFull sample        -0.1444    0.2804  -0.5149  0.6066    -0.6938 
## name                     -2.2342    1.4217  -1.5715  0.1161    -5.0206 
## depthsSubsurface:name     0.8392    2.5605   0.3277  0.7431    -4.1793 
## depthsDeep:name         -66.9869  106.2811  -0.6303  0.5285  -275.2942 
## depthsFull sample:name    1.7043    2.1938   0.7769  0.4372    -2.5955 
##                            ci.ub    
## intrcpt                   0.4538  . 
## depthsSubsurface          0.2198    
## depthsDeep               11.7512    
## depthsFull sample         0.4051    
## name                      0.5522    
## depthsSubsurface:name     5.8576    
## depthsDeep:name         141.3203    
## depthsFull sample:name    6.0041    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0194  0.1394     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 107.4958, p-val = 0.0003
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 5.1053, p-val = 0.6471
## 
## Model Results:
## 
##                         estimate        se     zval    pval      ci.lb 
## intrcpt                   0.2174    0.1206   1.8018  0.0716    -0.0191 
## depthsSubsurface         -0.1084    0.1675  -0.6475  0.5173    -0.4367 
## depthsDeep                2.8219    4.5559   0.6194  0.5357    -6.1074 
## depthsFull sample        -0.1444    0.2804  -0.5149  0.6066    -0.6938 
## cvel                     -2.2342    1.4217  -1.5715  0.1161    -5.0206 
## depthsSubsurface:cvel     0.8392    2.5605   0.3277  0.7431    -4.1793 
## depthsDeep:cvel         -66.9869  106.2811  -0.6303  0.5285  -275.2942 
## depthsFull sample:cvel    1.7043    2.1938   0.7769  0.4372    -2.5955 
##                            ci.ub    
## intrcpt                   0.4538  . 
## depthsSubsurface          0.2198    
## depthsDeep               11.7512    
## depthsFull sample         0.4051    
## cvel                      0.5522    
## depthsSubsurface:cvel     5.8576    
## depthsDeep:cvel         141.3203    
## depthsFull sample:cvel    6.0041    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0194  0.1394     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 107.4958, p-val = 0.0003
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 5.3015, p-val = 0.7249
## 
## Model Results:
## 
##                         estimate        se     zval    pval      ci.lb 
## depthsSurface             0.2174    0.1206   1.8018  0.0716    -0.0191 
## depthsSubsurface          0.1089    0.1753   0.6212  0.5345    -0.2347 
## depthsDeep                3.0393    4.5503   0.6679  0.5042    -5.8792 
## depthsFull sample         0.0730    0.2267   0.3221  0.7474    -0.3713 
## cvel                     -2.2342    1.4217  -1.5715  0.1161    -5.0206 
## depthsSubsurface:cvel     0.8392    2.5605   0.3277  0.7431    -4.1793 
## depthsDeep:cvel         -66.9869  106.2811  -0.6303  0.5285  -275.2942 
## depthsFull sample:cvel    1.7043    2.1938   0.7769  0.4372    -2.5955 
##                            ci.ub    
## depthsSurface             0.4538  . 
## depthsSubsurface          0.4526    
## depthsDeep               11.9577    
## depthsFull sample         0.5173    
## cvel                      0.5522    
## depthsSubsurface:cvel     5.8576    
## depthsDeep:cvel         141.3203    
## depthsFull sample:cvel    6.0041    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.4 Water depth

-> No significant relation with TOC/TN lnrr and water depth.

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0181  0.1347     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 63) = 114.0211, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 5.6973, p-val = 0.5755
## 
## Model Results:
## 
##                         estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  -0.0086  0.0570  -0.1506  0.8803  -0.1204  0.1032    
## depthsSubsurface         -0.0231  0.0772  -0.2991  0.7649  -0.1743  0.1282    
## depthsDeep               -0.0286  0.0890  -0.3216  0.7478  -0.2030  0.1458    
## depthsFull sample         0.2491  0.1819   1.3693  0.1709  -0.1074  0.6056    
## name                      0.0004  0.0003   1.4839  0.1378  -0.0001  0.0009    
## depthsSubsurface:name    -0.0002  0.0002  -0.7445  0.4566  -0.0006  0.0003    
## depthsDeep:name          -0.0001  0.0002  -0.3590  0.7196  -0.0005  0.0004    
## depthsFull sample:name   -0.0171  0.0131  -1.3029  0.1926  -0.0428  0.0086    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0181  0.1347     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 63) = 114.0211, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 5.6973, p-val = 0.5755
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                      -0.0086  0.0570  -0.1506  0.8803  -0.1204  0.1032 
## depthsSubsurface             -0.0231  0.0772  -0.2991  0.7649  -0.1743  0.1282 
## depthsDeep                   -0.0286  0.0890  -0.3216  0.7478  -0.2030  0.1458 
## depthsFull sample             0.2491  0.1819   1.3693  0.1709  -0.1074  0.6056 
## watdepth                      0.0004  0.0003   1.4839  0.1378  -0.0001  0.0009 
## depthsSubsurface:watdepth    -0.0002  0.0002  -0.7445  0.4566  -0.0006  0.0003 
## depthsDeep:watdepth          -0.0001  0.0002  -0.3590  0.7196  -0.0005  0.0004 
## depthsFull sample:watdepth   -0.0171  0.0131  -1.3029  0.1926  -0.0428  0.0086 
##                               
## intrcpt                       
## depthsSubsurface              
## depthsDeep                    
## depthsFull sample             
## watdepth                      
## depthsSubsurface:watdepth     
## depthsDeep:watdepth           
## depthsFull sample:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0181  0.1347     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 63) = 114.0211, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 5.7452, p-val = 0.6757
## 
## Model Results:
## 
##                             estimate      se     zval    pval    ci.lb   ci.ub 
## depthsSurface                -0.0086  0.0570  -0.1506  0.8803  -0.1204  0.1032 
## depthsSubsurface             -0.0317  0.0827  -0.3832  0.7016  -0.1937  0.1303 
## depthsDeep                   -0.0372  0.1005  -0.3700  0.7114  -0.2343  0.1599 
## depthsFull sample             0.2405  0.1972   1.2196  0.2226  -0.1460  0.6270 
## watdepth                      0.0004  0.0003   1.4839  0.1378  -0.0001  0.0009 
## depthsSubsurface:watdepth    -0.0002  0.0002  -0.7445  0.4566  -0.0006  0.0003 
## depthsDeep:watdepth          -0.0001  0.0002  -0.3590  0.7196  -0.0005  0.0004 
## depthsFull sample:watdepth   -0.0171  0.0131  -1.3029  0.1926  -0.0428  0.0086 
##                               
## depthsSurface                 
## depthsSubsurface              
## depthsDeep                    
## depthsFull sample             
## watdepth                      
## depthsSubsurface:watdepth     
## depthsDeep:watdepth           
## depthsFull sample:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.5 Primary productivity

-> No significant relation with TOC/TN lnrr and primary productivity.

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0219  0.1479     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 111.6786, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 4.3875, p-val = 0.7342
## 
## Model Results:
## 
##                         estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                   0.1288   0.0808   1.5940  0.1109   -0.0296   0.2872 
## depthsSubsurface         -0.1099   0.0778  -1.4117  0.1580   -0.2624   0.0427 
## depthsDeep               -0.0566   0.0752  -0.7524  0.4518   -0.2039   0.0908 
## depthsFull sample        -0.1698   0.1600  -1.0613  0.2885   -0.4834   0.1438 
## name                     -8.5944   5.3482  -1.6070  0.1081  -19.0767   1.8878 
## depthsSubsurface:name    12.9310  19.0913   0.6773  0.4982  -24.4873  50.3492 
## depthsDeep:name           1.4071   5.6099   0.2508  0.8020   -9.5881  12.4022 
## depthsFull sample:name    9.1016   6.3744   1.4278  0.1533   -3.3919  21.5952 
##                           
## intrcpt                   
## depthsSubsurface          
## depthsDeep                
## depthsFull sample         
## name                      
## depthsSubsurface:name     
## depthsDeep:name           
## depthsFull sample:name    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0219  0.1479     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 111.6786, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 4.3875, p-val = 0.7342
## 
## Model Results:
## 
##                            estimate       se     zval    pval     ci.lb 
## intrcpt                      0.1288   0.0808   1.5940  0.1109   -0.0296 
## depthsSubsurface            -0.1099   0.0778  -1.4117  0.1580   -0.2624 
## depthsDeep                  -0.0566   0.0752  -0.7524  0.4518   -0.2039 
## depthsFull sample           -0.1698   0.1600  -1.0613  0.2885   -0.4834 
## nppsurf                     -8.5944   5.3482  -1.6070  0.1081  -19.0767 
## depthsSubsurface:nppsurf    12.9310  19.0913   0.6773  0.4982  -24.4873 
## depthsDeep:nppsurf           1.4071   5.6099   0.2508  0.8020   -9.5881 
## depthsFull sample:nppsurf    9.1016   6.3744   1.4278  0.1533   -3.3919 
##                              ci.ub    
## intrcpt                     0.2872    
## depthsSubsurface            0.0427    
## depthsDeep                  0.0908    
## depthsFull sample           0.1438    
## nppsurf                     1.8878    
## depthsSubsurface:nppsurf   50.3492    
## depthsDeep:nppsurf         12.4022    
## depthsFull sample:nppsurf  21.5952    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 70; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0219  0.1479     14     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 62) = 111.6786, p-val = 0.0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 4.5713, p-val = 0.8023
## 
## Model Results:
## 
##                            estimate       se     zval    pval     ci.lb 
## depthsSurface                0.1288   0.0808   1.5940  0.1109   -0.0296 
## depthsSubsurface             0.0189   0.1017   0.1863  0.8522   -0.1804 
## depthsDeep                   0.0723   0.0981   0.7368  0.4613   -0.1200 
## depthsFull sample           -0.0410   0.1266  -0.3241  0.7459   -0.2891 
## nppsurf                     -8.5944   5.3482  -1.6070  0.1081  -19.0767 
## depthsSubsurface:nppsurf    12.9310  19.0913   0.6773  0.4982  -24.4873 
## depthsDeep:nppsurf           1.4071   5.6099   0.2508  0.8020   -9.5881 
## depthsFull sample:nppsurf    9.1016   6.3744   1.4278  0.1533   -3.3919 
##                              ci.ub    
## depthsSurface               0.2872    
## depthsSubsurface            0.2183    
## depthsDeep                  0.2645    
## depthsFull sample           0.2070    
## nppsurf                     1.8878    
## depthsSubsurface:nppsurf   50.3492    
## depthsDeep:nppsurf         12.4022    
## depthsFull sample:nppsurf  21.5952    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.6 Season

-> No significant differences between seasons

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0082  0.0906     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 55) = 76.8068, p-val = 0.0277
## 
## Test of Moderators (coefficients 2:16):
## QM(df = 15) = 19.3045, p-val = 0.2003
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## intrcpt                            0.1318  0.1165   1.1313  0.2579  -0.0965 
## depthsSubsurface                   0.2147  0.1654   1.2977  0.1944  -0.1095 
## depthsDeep                        -0.1477  0.3162  -0.4672  0.6403  -0.7674 
## depthsFull sample                  0.0659  0.0747   0.8824  0.3776  -0.0805 
## hseasonDry                        -0.2813  0.1568  -1.7937  0.0729  -0.5886 
## hseasonSpring                     -0.1451  0.1314  -1.1047  0.2693  -0.4026 
## hseasonSummer                     -0.1195  0.1189  -1.0051  0.3149  -0.3525 
## hseasonWet                        -0.3288  0.1612  -2.0392  0.0414  -0.6448 
## hseasonWinter                     -0.0027  0.1276  -0.0212  0.9831  -0.2528 
## hseasonYearly                     -0.3364  0.1807  -1.8617  0.0626  -0.6906 
## depthsSubsurface:hseasonSpring    -0.3112  0.1819  -1.7103  0.0872  -0.6678 
## depthsDeep:hseasonSpring           0.1502  0.3306   0.4542  0.6497  -0.4977 
## depthsFull sample:hseasonSpring   -0.8865  1.4744  -0.6013  0.5477  -3.7762 
## depthsSubsurface:hseasonSummer    -0.2887  0.1741  -1.6584  0.0972  -0.6299 
## depthsDeep:hseasonSummer           0.1322  0.3226   0.4098  0.6819  -0.5000 
## depthsDeep:hseasonWinter           0.0272  0.3370   0.0806  0.9357  -0.6333 
##                                    ci.ub    
## intrcpt                           0.3600    
## depthsSubsurface                  0.5389    
## depthsDeep                        0.4720    
## depthsFull sample                 0.2124    
## hseasonDry                        0.0261  . 
## hseasonSpring                     0.1124    
## hseasonSummer                     0.1135    
## hseasonWet                       -0.0128  * 
## hseasonWinter                     0.2474    
## hseasonYearly                     0.0177  . 
## depthsSubsurface:hseasonSpring    0.0454  . 
## depthsDeep:hseasonSpring          0.7980    
## depthsFull sample:hseasonSpring   2.0032    
## depthsSubsurface:hseasonSummer    0.0525  . 
## depthsDeep:hseasonSummer          0.7644    
## depthsDeep:hseasonWinter          0.6876    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0082  0.0906     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 55) = 76.8068, p-val = 0.0277
## 
## Test of Moderators (coefficients 1:16):
## QM(df = 16) = 19.3493, p-val = 0.2510
## 
## Model Results:
## 
##                                  estimate      se     zval    pval    ci.lb 
## depthsSurface                      0.1318  0.1165   1.1313  0.2579  -0.0965 
## depthsSubsurface                   0.3464  0.1202   2.8813  0.0040   0.1108 
## depthsDeep                        -0.0160  0.2951  -0.0541  0.9568  -0.5943 
## depthsFull sample                  0.1977  0.1358   1.4554  0.1455  -0.0685 
## hseasonDry                        -0.2813  0.1568  -1.7937  0.0729  -0.5886 
## hseasonSpring                     -0.1451  0.1314  -1.1047  0.2693  -0.4026 
## hseasonSummer                     -0.1195  0.1189  -1.0051  0.3149  -0.3525 
## hseasonWet                        -0.3288  0.1612  -2.0392  0.0414  -0.6448 
## hseasonWinter                     -0.0027  0.1276  -0.0212  0.9831  -0.2528 
## hseasonYearly                     -0.3364  0.1807  -1.8617  0.0626  -0.6906 
## depthsSubsurface:hseasonSpring    -0.3112  0.1819  -1.7103  0.0872  -0.6678 
## depthsDeep:hseasonSpring           0.1502  0.3306   0.4542  0.6497  -0.4977 
## depthsFull sample:hseasonSpring   -0.8865  1.4744  -0.6013  0.5477  -3.7762 
## depthsSubsurface:hseasonSummer    -0.2887  0.1741  -1.6584  0.0972  -0.6299 
## depthsDeep:hseasonSummer           0.1322  0.3226   0.4098  0.6819  -0.5000 
## depthsDeep:hseasonWinter           0.0272  0.3370   0.0806  0.9357  -0.6333 
##                                    ci.ub     
## depthsSurface                     0.3600     
## depthsSubsurface                  0.5821  ** 
## depthsDeep                        0.5624     
## depthsFull sample                 0.4639     
## hseasonDry                        0.0261   . 
## hseasonSpring                     0.1124     
## hseasonSummer                     0.1135     
## hseasonWet                       -0.0128   * 
## hseasonWinter                     0.2474     
## hseasonYearly                     0.0177   . 
## depthsSubsurface:hseasonSpring    0.0454   . 
## depthsDeep:hseasonSpring          0.7980     
## depthsFull sample:hseasonSpring   2.0032     
## depthsSubsurface:hseasonSummer    0.0525   . 
## depthsDeep:hseasonSummer          0.7644     
## depthsDeep:hseasonWinter          0.6876     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.7 Habitat

-> No signficant habitat differences.

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0214  0.1462     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 64) = 113.6884, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:7):
## QM(df = 6) = 3.6151, p-val = 0.7286
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                           0.0912  0.0691   1.3194  0.1870  -0.0443 
## depthsSubsurface                 -0.0841  0.0562  -1.4973  0.1343  -0.1942 
## depthsDeep                       -0.0449  0.0534  -0.8412  0.4003  -0.1495 
## depthsFull sample                -0.1022  0.1693  -0.6040  0.5458  -0.4340 
## hhabtypeSand                     -0.1374  0.0973  -1.4119  0.1580  -0.3282 
## depthsSubsurface:hhabtypeSand     0.0943  0.0984   0.9583  0.3379  -0.0986 
## depthsFull sample:hhabtypeSand    0.1499  0.1845   0.8126  0.4164  -0.2117 
##                                  ci.ub    
## intrcpt                         0.2266    
## depthsSubsurface                0.0260    
## depthsDeep                      0.0597    
## depthsFull sample               0.2295    
## hhabtypeSand                    0.0533    
## depthsSubsurface:hhabtypeSand   0.2873    
## depthsFull sample:hhabtypeSand  0.5116    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 71; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0214  0.1462     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 64) = 113.6884, p-val = 0.0001
## 
## Test of Moderators (coefficients 1:7):
## QM(df = 7) = 3.6588, p-val = 0.8181
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                     0.0912  0.0691   1.3194  0.1870  -0.0443 
## depthsSubsurface                  0.0071  0.0729   0.0968  0.9229  -0.1359 
## depthsDeep                        0.0463  0.0753   0.6143  0.5390  -0.1014 
## depthsFull sample                -0.0111  0.1545  -0.0716  0.9429  -0.3139 
## hhabtypeSand                     -0.1374  0.0973  -1.4119  0.1580  -0.3282 
## depthsSubsurface:hhabtypeSand     0.0943  0.0984   0.9583  0.3379  -0.0986 
## depthsFull sample:hhabtypeSand    0.1499  0.1845   0.8126  0.4164  -0.2117 
##                                  ci.ub    
## depthsSurface                   0.2266    
## depthsSubsurface                0.1500    
## depthsDeep                      0.1940    
## depthsFull sample               0.2918    
## hhabtypeSand                    0.0533    
## depthsSubsurface:hhabtypeSand   0.2873    
## depthsFull sample:hhabtypeSand  0.5116    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4 Chl-a

–> Slice depth no significant rpedictor of Chla LnRR, but LnRR significantly more negative in surface layer.

## 
## Mixed-Effects Model (k = 33; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0236 (SE = 0.0204)
## tau (square root of estimated tau^2 value):             0.1536
## I^2 (residual heterogeneity / unaccounted variability): 30.73%
## H^2 (unaccounted variability / sampling variability):   1.44
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 40.6369, p-val = 0.0580
## 
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 3.0194, p-val = 0.5546
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb    ci.ub     
## intrcpt             -0.2027  0.0701  -2.8933  0.0038  -0.3400  -0.0654  ** 
## depthsSubsurface     0.1806  0.1366   1.3223  0.1861  -0.0871   0.4483     
## depthsDeep           0.1665  0.1549   1.0746  0.2826  -0.1372   0.4702     
## depthsVery deep      0.2241  0.2017   1.1107  0.2667  -0.1713   0.6194     
## depthsFull sample    0.0286  0.5651   0.0505  0.9597  -1.0790   1.1361     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 33; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0236 (SE = 0.0204)
## tau (square root of estimated tau^2 value):             0.1536
## I^2 (residual heterogeneity / unaccounted variability): 30.73%
## H^2 (unaccounted variability / sampling variability):   1.44
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 40.6369, p-val = 0.0580
## 
## Test of Moderators (coefficients 1:5):
## QM(df = 5) = 8.5845, p-val = 0.1268
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb    ci.ub     
## depthsSurface       -0.2027  0.0701  -2.8933  0.0038  -0.3400  -0.0654  ** 
## depthsSubsurface    -0.0221  0.1172  -0.1883  0.8506  -0.2519   0.2077     
## depthsDeep          -0.0362  0.1382  -0.2619  0.7934  -0.3070   0.2347     
## depthsVery deep      0.0214  0.1892   0.1130  0.9100  -0.3494   0.3922     
## depthsFull sample   -0.1741  0.5607  -0.3105  0.7562  -1.2731   0.9249     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.1 Time since trawling

–> Significantly more negative LnRR for Chla in surface and deep strata for lower (shorter) time since trawl values.

## 
## Multivariate Meta-Analysis Model (k = 118; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0823  0.2868     13     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 110) = 383.8055, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 57.7822, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.4854  0.0983  -4.9376  <.0001  -0.6781  -0.2927  *** 
## depthsSubsurface         0.2999  0.1155   2.5966  0.0094   0.0735   0.5263   ** 
## depthsDeep               0.4054  0.0843   4.8101  <.0001   0.2402   0.5706  *** 
## depthsVery deep          0.4267  0.1215   3.5124  0.0004   0.1886   0.6649  *** 
## name                     0.0851  0.0179   4.7532  <.0001   0.0500   0.1201  *** 
## depthsSubsurface:name   -0.0122  0.0593  -0.2063  0.8365  -0.1285   0.1041      
## depthsDeep:name         -0.0768  0.0342  -2.2481  0.0246  -0.1438  -0.0098    * 
## depthsVery deep:name    -0.0349  0.0613  -0.5685  0.5697  -0.1551   0.0853      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 118; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0823  0.2868     13     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 110) = 383.8055, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 57.7822, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## intrcpt                                    -0.4854  0.0983  -4.9376  <.0001 
## depthsSubsurface                            0.2999  0.1155   2.5966  0.0094 
## depthsDeep                                  0.4054  0.0843   4.8101  <.0001 
## depthsVery deep                             0.4267  0.1215   3.5124  0.0004 
## log(timesincetrawl + 1)                     0.0851  0.0179   4.7532  <.0001 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0122  0.0593  -0.2063  0.8365 
## depthsDeep:log(timesincetrawl + 1)         -0.0768  0.0342  -2.2481  0.0246 
## depthsVery deep:log(timesincetrawl + 1)    -0.0349  0.0613  -0.5685  0.5697 
##                                             ci.lb    ci.ub      
## intrcpt                                   -0.6781  -0.2927  *** 
## depthsSubsurface                           0.0735   0.5263   ** 
## depthsDeep                                 0.2402   0.5706  *** 
## depthsVery deep                            0.1886   0.6649  *** 
## log(timesincetrawl + 1)                    0.0500   0.1201  *** 
## depthsSubsurface:log(timesincetrawl + 1)  -0.1285   0.1041      
## depthsDeep:log(timesincetrawl + 1)        -0.1438  -0.0098    * 
## depthsVery deep:log(timesincetrawl + 1)   -0.1551   0.0853      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 118; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0823  0.2868     13     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 110) = 383.8055, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 66.1050, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## depthsSurface                              -0.4854  0.0983  -4.9376  <.0001 
## depthsSubsurface                           -0.1855  0.1397  -1.3277  0.1843 
## depthsDeep                                 -0.0800  0.1156  -0.6924  0.4887 
## depthsVery deep                            -0.0587  0.1439  -0.4077  0.6835 
## log(timesincetrawl + 1)                     0.0851  0.0179   4.7532  <.0001 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0122  0.0593  -0.2063  0.8365 
## depthsDeep:log(timesincetrawl + 1)         -0.0768  0.0342  -2.2481  0.0246 
## depthsVery deep:log(timesincetrawl + 1)    -0.0349  0.0613  -0.5685  0.5697 
##                                             ci.lb    ci.ub      
## depthsSurface                             -0.6781  -0.2927  *** 
## depthsSubsurface                          -0.4594   0.0883      
## depthsDeep                                -0.3066   0.1465      
## depthsVery deep                           -0.3408   0.2234      
## log(timesincetrawl + 1)                    0.0500   0.1201  *** 
## depthsSubsurface:log(timesincetrawl + 1)  -0.1285   0.1041      
## depthsDeep:log(timesincetrawl + 1)        -0.1438  -0.0098    * 
## depthsVery deep:log(timesincetrawl + 1)   -0.1551   0.0853      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.2 Trawling frequency

-> No significant relations with tralwing frequency.

## 
## Multivariate Meta-Analysis Model (k = 80; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1122  0.3349     12     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 72) = 124.0438, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 34.4366, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.4115  0.1449  -2.8400  0.0045  -0.6955  -0.1275   ** 
## depthsSubsurface         0.3432  0.0836   4.1048  <.0001   0.1793   0.5071  *** 
## depthsDeep               0.3420  0.0796   4.2941  <.0001   0.1859   0.4981  *** 
## depthsVery deep          0.4138  0.0813   5.0874  <.0001   0.2544   0.5732  *** 
## name                     0.0235  0.0257   0.9165  0.3594  -0.0268   0.0739      
## depthsSubsurface:name   -0.0301  0.0234  -1.2836  0.1993  -0.0760   0.0159      
## depthsDeep:name         -0.0366  0.0262  -1.3994  0.1617  -0.0879   0.0147      
## depthsVery deep:name    -0.0169  0.0271  -0.6245  0.5323  -0.0701   0.0362      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 80; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1122  0.3349     12     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 72) = 124.0438, p-val = 0.0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 34.4366, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb 
## intrcpt                       -0.4115  0.1449  -2.8400  0.0045  -0.6955 
## depthsSubsurface               0.3432  0.0836   4.1048  <.0001   0.1793 
## depthsDeep                     0.3420  0.0796   4.2941  <.0001   0.1859 
## depthsVery deep                0.4138  0.0813   5.0874  <.0001   0.2544 
## heffortnum                     0.0235  0.0257   0.9165  0.3594  -0.0268 
## depthsSubsurface:heffortnum   -0.0301  0.0234  -1.2836  0.1993  -0.0760 
## depthsDeep:heffortnum         -0.0366  0.0262  -1.3994  0.1617  -0.0879 
## depthsVery deep:heffortnum    -0.0169  0.0271  -0.6245  0.5323  -0.0701 
##                                ci.ub      
## intrcpt                      -0.1275   ** 
## depthsSubsurface              0.5071  *** 
## depthsDeep                    0.4981  *** 
## depthsVery deep               0.5732  *** 
## heffortnum                    0.0739      
## depthsSubsurface:heffortnum   0.0159      
## depthsDeep:heffortnum         0.0147      
## depthsVery deep:heffortnum    0.0362      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 80; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1122  0.3349     12     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 72) = 124.0438, p-val = 0.0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 39.2818, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb 
## depthsSurface                 -0.4115  0.1449  -2.8400  0.0045  -0.6955 
## depthsSubsurface              -0.0683  0.1484  -0.4602  0.6454  -0.3592 
## depthsDeep                    -0.0695  0.1461  -0.4755  0.6345  -0.3559 
## depthsVery deep                0.0023  0.1475   0.0155  0.9877  -0.2868 
## heffortnum                     0.0235  0.0257   0.9165  0.3594  -0.0268 
## depthsSubsurface:heffortnum   -0.0301  0.0234  -1.2836  0.1993  -0.0760 
## depthsDeep:heffortnum         -0.0366  0.0262  -1.3994  0.1617  -0.0879 
## depthsVery deep:heffortnum    -0.0169  0.0271  -0.6245  0.5323  -0.0701 
##                                ci.ub     
## depthsSurface                -0.1275  ** 
## depthsSubsurface              0.2226     
## depthsDeep                    0.2169     
## depthsVery deep               0.2914     
## heffortnum                    0.0739     
## depthsSubsurface:heffortnum   0.0159     
## depthsDeep:heffortnum         0.0147     
## depthsVery deep:heffortnum    0.0362     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.3 Current velocity

-> Singificant regression for chla and current velocity in surface samples, more negative lnrr towards high velocity areas.

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1713  0.4139     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 434.5088, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 38.7753, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval     ci.lb    ci.ub    
## intrcpt                  0.1370  0.2358   0.5813  0.5611   -0.3251   0.5991    
## depthsSubsurface        -0.2047  0.3117  -0.6566  0.5114   -0.8155   0.4062    
## depthsDeep              -0.0172  0.2281  -0.0755  0.9398   -0.4643   0.4298    
## depthsVery deep         -0.0895  0.4078  -0.2194  0.8264   -0.8888   0.7099    
## name                    -6.0450  2.5761  -2.3466  0.0189  -11.0940  -0.9960  * 
## depthsSubsurface:name    6.3180  3.8878   1.6251  0.1041   -1.3020  13.9380    
## depthsDeep:name          3.9566  3.0130   1.3132  0.1891   -1.9486   9.8619    
## depthsVery deep:name     6.0780  5.1389   1.1827  0.2369   -3.9941  16.1501    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1713  0.4139     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 434.5088, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 38.7753, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval     ci.lb    ci.ub    
## intrcpt                  0.1370  0.2358   0.5813  0.5611   -0.3251   0.5991    
## depthsSubsurface        -0.2047  0.3117  -0.6566  0.5114   -0.8155   0.4062    
## depthsDeep              -0.0172  0.2281  -0.0755  0.9398   -0.4643   0.4298    
## depthsVery deep         -0.0895  0.4078  -0.2194  0.8264   -0.8888   0.7099    
## cvel                    -6.0450  2.5761  -2.3466  0.0189  -11.0940  -0.9960  * 
## depthsSubsurface:cvel    6.3180  3.8878   1.6251  0.1041   -1.3020  13.9380    
## depthsDeep:cvel          3.9566  3.0130   1.3132  0.1891   -1.9486   9.8619    
## depthsVery deep:cvel     6.0780  5.1389   1.1827  0.2369   -3.9941  16.1501    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1713  0.4139     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 434.5088, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 42.2362, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval     ci.lb    ci.ub    
## depthsSurface            0.1370  0.2358   0.5813  0.5611   -0.3251   0.5991    
## depthsSubsurface        -0.0676  0.3655  -0.1850  0.8533   -0.7840   0.6488    
## depthsDeep               0.1198  0.3069   0.3904  0.6962   -0.4816   0.7213    
## depthsVery deep          0.0476  0.4488   0.1060  0.9155   -0.8320   0.9272    
## cvel                    -6.0450  2.5761  -2.3466  0.0189  -11.0940  -0.9960  * 
## depthsSubsurface:cvel    6.3180  3.8878   1.6251  0.1041   -1.3020  13.9380    
## depthsDeep:cvel          3.9566  3.0130   1.3132  0.1891   -1.9486   9.8619    
## depthsVery deep:cvel     6.0780  5.1389   1.1827  0.2369   -3.9941  16.1501    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.4 Water depth

-> Significant regressions for different depth strata and water depth, but not always with the same sign.

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2353  0.4851     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 125) = 447.8626, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 89.3604, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.6319  0.1381  -4.5767  <.0001  -0.9025  -0.3613  *** 
## depthsSubsurface         0.3495  0.0756   4.6260  <.0001   0.2014   0.4976  *** 
## depthsDeep               0.4233  0.0915   4.6239  <.0001   0.2439   0.6027  *** 
## depthsVery deep          0.3769  0.1541   2.4460  0.0144   0.0749   0.6789    * 
## name                     0.0066  0.0009   7.2532  <.0001   0.0048   0.0084  *** 
## depthsSubsurface:name   -0.0017  0.0006  -2.9242  0.0035  -0.0028  -0.0006   ** 
## depthsDeep:name         -0.0069  0.0031  -2.2172  0.0266  -0.0129  -0.0008    * 
## depthsVery deep:name     0.0036  0.0106   0.3373  0.7359  -0.0172   0.0244      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2353  0.4851     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 125) = 447.8626, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 89.3604, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                     -0.6319  0.1381  -4.5767  <.0001  -0.9025  -0.3613 
## depthsSubsurface             0.3495  0.0756   4.6260  <.0001   0.2014   0.4976 
## depthsDeep                   0.4233  0.0915   4.6239  <.0001   0.2439   0.6027 
## depthsVery deep              0.3769  0.1541   2.4460  0.0144   0.0749   0.6789 
## watdepth                     0.0066  0.0009   7.2532  <.0001   0.0048   0.0084 
## depthsSubsurface:watdepth   -0.0017  0.0006  -2.9242  0.0035  -0.0028  -0.0006 
## depthsDeep:watdepth         -0.0069  0.0031  -2.2172  0.0266  -0.0129  -0.0008 
## depthsVery deep:watdepth     0.0036  0.0106   0.3373  0.7359  -0.0172   0.0244 
##                                
## intrcpt                    *** 
## depthsSubsurface           *** 
## depthsDeep                 *** 
## depthsVery deep              * 
## watdepth                   *** 
## depthsSubsurface:watdepth   ** 
## depthsDeep:watdepth          * 
## depthsVery deep:watdepth       
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2353  0.4851     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 125) = 447.8626, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 92.2872, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## depthsSurface               -0.6319  0.1381  -4.5767  <.0001  -0.9025  -0.3613 
## depthsSubsurface            -0.2824  0.1505  -1.8763  0.0606  -0.5773   0.0126 
## depthsDeep                  -0.2086  0.1600  -1.3036  0.1924  -0.5223   0.1050 
## depthsVery deep             -0.2550  0.2079  -1.2265  0.2200  -0.6625   0.1525 
## watdepth                     0.0066  0.0009   7.2532  <.0001   0.0048   0.0084 
## depthsSubsurface:watdepth   -0.0017  0.0006  -2.9242  0.0035  -0.0028  -0.0006 
## depthsDeep:watdepth         -0.0069  0.0031  -2.2172  0.0266  -0.0129  -0.0008 
## depthsVery deep:watdepth     0.0036  0.0106   0.3373  0.7359  -0.0172   0.0244 
##                                
## depthsSurface              *** 
## depthsSubsurface             . 
## depthsDeep                     
## depthsVery deep                
## watdepth                   *** 
## depthsSubsurface:watdepth   ** 
## depthsDeep:watdepth          * 
## depthsVery deep:watdepth       
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.5 Primary productivity

–> Significant regression for different depth strata and surface primary productivity, more negative lnrr in areas towards higher NPP areas.

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0710  0.2664     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 429.0443, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 49.4335, p-val < .0001
## 
## Model Results:
## 
##                        estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                 -0.0848   0.1033  -0.8210  0.4117   -0.2873   0.1177 
## depthsSubsurface         0.3154   0.1001   3.1498  0.0016    0.1191   0.5117 
## depthsDeep               0.4213   0.0900   4.6788  <.0001    0.2448   0.5978 
## depthsVery deep          0.4238   0.1102   3.8439  0.0001    0.2077   0.6399 
## name                   -11.4073   3.6907  -3.0908  0.0020  -18.6408  -4.1737 
## depthsSubsurface:name   -0.4878  13.6595  -0.0357  0.9715  -27.2598  26.2843 
## depthsDeep:name        -20.4080   8.4079  -2.4272  0.0152  -36.8872  -3.9288 
## depthsVery deep:name    -2.4391  18.2885  -0.1334  0.8939  -38.2839  33.4058 
##                            
## intrcpt                    
## depthsSubsurface        ** 
## depthsDeep             *** 
## depthsVery deep        *** 
## name                    ** 
## depthsSubsurface:name      
## depthsDeep:name          * 
## depthsVery deep:name       
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0710  0.2664     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 429.0443, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 49.4335, p-val < .0001
## 
## Model Results:
## 
##                           estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                    -0.0848   0.1033  -0.8210  0.4117   -0.2873   0.1177 
## depthsSubsurface            0.3154   0.1001   3.1498  0.0016    0.1191   0.5117 
## depthsDeep                  0.4213   0.0900   4.6788  <.0001    0.2448   0.5978 
## depthsVery deep             0.4238   0.1102   3.8439  0.0001    0.2077   0.6399 
## nppsurf                   -11.4073   3.6907  -3.0908  0.0020  -18.6408  -4.1737 
## depthsSubsurface:nppsurf   -0.4878  13.6595  -0.0357  0.9715  -27.2598  26.2843 
## depthsDeep:nppsurf        -20.4080   8.4079  -2.4272  0.0152  -36.8872  -3.9288 
## depthsVery deep:nppsurf    -2.4391  18.2885  -0.1334  0.8939  -38.2839  33.4058 
##                               
## intrcpt                       
## depthsSubsurface           ** 
## depthsDeep                *** 
## depthsVery deep           *** 
## nppsurf                    ** 
## depthsSubsurface:nppsurf      
## depthsDeep:nppsurf          * 
## depthsVery deep:nppsurf       
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 125; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0710  0.2664     15     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 429.0443, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 55.0061, p-val < .0001
## 
## Model Results:
## 
##                           estimate       se     zval    pval     ci.lb    ci.ub 
## depthsSurface              -0.0848   0.1033  -0.8210  0.4117   -0.2873   0.1177 
## depthsSubsurface            0.2306   0.1424   1.6189  0.1055   -0.0486   0.5098 
## depthsDeep                  0.3365   0.1334   2.5222  0.0117    0.0750   0.5979 
## depthsVery deep             0.3390   0.1517   2.2348  0.0254    0.0417   0.6362 
## nppsurf                   -11.4073   3.6907  -3.0908  0.0020  -18.6408  -4.1737 
## depthsSubsurface:nppsurf   -0.4878  13.6595  -0.0357  0.9715  -27.2598  26.2843 
## depthsDeep:nppsurf        -20.4080   8.4079  -2.4272  0.0152  -36.8872  -3.9288 
## depthsVery deep:nppsurf    -2.4391  18.2885  -0.1334  0.8939  -38.2839  33.4058 
##                              
## depthsSurface                
## depthsSubsurface             
## depthsDeep                 * 
## depthsVery deep            * 
## nppsurf                   ** 
## depthsSubsurface:nppsurf     
## depthsDeep:nppsurf         * 
## depthsVery deep:nppsurf      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.6 Season

-> Significant differences between seasons, with lnrr in fall being lower.

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1101  0.3318     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 118) = 439.4436, p-val < .0001
## 
## Test of Moderators (coefficients 2:15):
## QM(df = 14) = 49.9357, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                          -0.3774  0.1010  -3.7356  0.0002  -0.5754 
## depthsSubsurface                  0.2417  0.0657   3.6800  0.0002   0.1130 
## depthsDeep                        0.3031  0.0675   4.4873  <.0001   0.1707 
## depthsVery deep                   0.3738  0.0691   5.4069  <.0001   0.2383 
## hseasonSpring                     0.1963  0.1013   1.9379  0.0526  -0.0022 
## hseasonSummer                     0.1157  0.0403   2.8741  0.0041   0.0368 
## hseasonWinter                     0.2757  0.1039   2.6544  0.0079   0.0721 
## depthsSubsurface:hseasonSpring   -0.1653  0.1369  -1.2072  0.2274  -0.4337 
## depthsDeep:hseasonSpring         -0.3543  0.1522  -2.3282  0.0199  -0.6526 
## depthsVery deep:hseasonSpring     0.0327  0.3208   0.1019  0.9188  -0.5961 
## depthsSubsurface:hseasonSummer   -0.0891  0.1311  -0.6799  0.4965  -0.3460 
## depthsDeep:hseasonSummer          0.1230  0.4976   0.2471  0.8048  -0.8523 
## depthsVery deep:hseasonSummer    -0.5408  0.6162  -0.8776  0.3802  -1.7486 
## depthsSubsurface:hseasonWinter    0.0359  0.1885   0.1903  0.8491  -0.3336 
## depthsDeep:hseasonWinter         -0.4003  0.2245  -1.7830  0.0746  -0.8404 
##                                   ci.ub      
## intrcpt                         -0.1794  *** 
## depthsSubsurface                 0.3704  *** 
## depthsDeep                       0.4354  *** 
## depthsVery deep                  0.5093  *** 
## hseasonSpring                    0.3948    . 
## hseasonSummer                    0.1946   ** 
## hseasonWinter                    0.4792   ** 
## depthsSubsurface:hseasonSpring   0.1031      
## depthsDeep:hseasonSpring        -0.0560    * 
## depthsVery deep:hseasonSpring    0.6614      
## depthsSubsurface:hseasonSummer   0.1678      
## depthsDeep:hseasonSummer         1.0983      
## depthsVery deep:hseasonSummer    0.6670      
## depthsSubsurface:hseasonWinter   0.4053      
## depthsDeep:hseasonWinter         0.0397    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1101  0.3318     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 118) = 439.4436, p-val < .0001
## 
## Test of Moderators (coefficients 1:15):
## QM(df = 15) = 54.7027, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                    -0.3774  0.1010  -3.7356  0.0002  -0.5754 
## depthsSubsurface                 -0.1357  0.1134  -1.1966  0.2315  -0.3581 
## depthsDeep                       -0.0744  0.1160  -0.6407  0.5217  -0.3018 
## depthsVery deep                  -0.0036  0.1170  -0.0310  0.9752  -0.2329 
## hseasonSpring                     0.1963  0.1013   1.9379  0.0526  -0.0022 
## hseasonSummer                     0.1157  0.0403   2.8741  0.0041   0.0368 
## hseasonWinter                     0.2757  0.1039   2.6544  0.0079   0.0721 
## depthsSubsurface:hseasonSpring   -0.1653  0.1369  -1.2072  0.2274  -0.4337 
## depthsDeep:hseasonSpring         -0.3543  0.1522  -2.3282  0.0199  -0.6526 
## depthsVery deep:hseasonSpring     0.0327  0.3208   0.1019  0.9188  -0.5961 
## depthsSubsurface:hseasonSummer   -0.0891  0.1311  -0.6799  0.4965  -0.3460 
## depthsDeep:hseasonSummer          0.1230  0.4976   0.2471  0.8048  -0.8523 
## depthsVery deep:hseasonSummer    -0.5408  0.6162  -0.8776  0.3802  -1.7486 
## depthsSubsurface:hseasonWinter    0.0359  0.1885   0.1903  0.8491  -0.3336 
## depthsDeep:hseasonWinter         -0.4003  0.2245  -1.7830  0.0746  -0.8404 
##                                   ci.ub      
## depthsSurface                   -0.1794  *** 
## depthsSubsurface                 0.0866      
## depthsDeep                       0.1531      
## depthsVery deep                  0.2256      
## hseasonSpring                    0.3948    . 
## hseasonSummer                    0.1946   ** 
## hseasonWinter                    0.4792   ** 
## depthsSubsurface:hseasonSpring   0.1031      
## depthsDeep:hseasonSpring        -0.0560    * 
## depthsVery deep:hseasonSpring    0.6614      
## depthsSubsurface:hseasonSummer   0.1678      
## depthsDeep:hseasonSummer         1.0983      
## depthsVery deep:hseasonSummer    0.6670      
## depthsSubsurface:hseasonWinter   0.4053      
## depthsDeep:hseasonWinter         0.0397    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.7 Habitat

-> No significant difference between habitat.

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1149  0.3390     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 125) = 454.1169, p-val < .0001
## 
## Test of Moderators (coefficients 2:8):
## QM(df = 7) = 29.6205, p-val = 0.0001
## 
## Model Results:
## 
##                                estimate      se     zval    pval    ci.lb 
## intrcpt                         -0.3405  0.1390  -2.4492  0.0143  -0.6130 
## depthsSubsurface                 0.1683  0.0546   3.0837  0.0020   0.0613 
## depthsDeep                       0.2208  0.0578   3.8231  0.0001   0.1076 
## depthsVery deep                  0.3088  0.0628   4.9137  <.0001   0.1856 
## hhabtypeSand                     0.1278  0.1956   0.6534  0.5135  -0.2555 
## depthsSubsurface:hhabtypeSand    0.0811  0.1724   0.4702  0.6382  -0.2569 
## depthsDeep:hhabtypeSand         -0.2754  0.2596  -1.0611  0.2887  -0.7842 
## depthsVery deep:hhabtypeSand     0.1683  0.4210   0.3997  0.6894  -0.6569 
##                                  ci.ub      
## intrcpt                        -0.0680    * 
## depthsSubsurface                0.2752   ** 
## depthsDeep                      0.3340  *** 
## depthsVery deep                 0.4319  *** 
## hhabtypeSand                    0.5111      
## depthsSubsurface:hhabtypeSand   0.4191      
## depthsDeep:hhabtypeSand         0.2333      
## depthsVery deep:hhabtypeSand    0.9934      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 133; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1149  0.3390     16     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 125) = 454.1169, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## QM(df = 8) = 34.2721, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se     zval    pval    ci.lb 
## depthsSurface                   -0.3405  0.1390  -2.4492  0.0143  -0.6130 
## depthsSubsurface                -0.1723  0.1437  -1.1991  0.2305  -0.4538 
## depthsDeep                      -0.1197  0.1442  -0.8303  0.4064  -0.4024 
## depthsVery deep                 -0.0318  0.1462  -0.2173  0.8280  -0.3183 
## hhabtypeSand                     0.1278  0.1956   0.6534  0.5135  -0.2555 
## depthsSubsurface:hhabtypeSand    0.0811  0.1724   0.4702  0.6382  -0.2569 
## depthsDeep:hhabtypeSand         -0.2754  0.2596  -1.0611  0.2887  -0.7842 
## depthsVery deep:hhabtypeSand     0.1683  0.4210   0.3997  0.6894  -0.6569 
##                                  ci.ub    
## depthsSurface                  -0.0680  * 
## depthsSubsurface                0.1093    
## depthsDeep                      0.1629    
## depthsVery deep                 0.2548    
## hhabtypeSand                    0.5111    
## depthsSubsurface:hhabtypeSand   0.4191    
## depthsDeep:hhabtypeSand         0.2333    
## depthsVery deep:hhabtypeSand    0.9934    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5 Phaeopigments

-> Slice depth no overall predictor of lnrr of phaeopigments, but LnRR values at the surface significantly more negative than deeper strata.

## 
## Mixed-Effects Model (k = 21; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0456 (SE = 0.0241)
## tau (square root of estimated tau^2 value):             0.2136
## I^2 (residual heterogeneity / unaccounted variability): 70.06%
## H^2 (unaccounted variability / sampling variability):   3.34
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 59.7397, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 2.0819, p-val = 0.5556
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt            -0.3280  0.0843  -3.8912  <.0001  -0.4932  -0.1628  *** 
## depthsSubsurface    0.1599  0.1415   1.1303  0.2584  -0.1174   0.4372      
## depthsDeep          0.1999  0.1828   1.0938  0.2740  -0.1583   0.5582      
## depthsVery deep     0.1462  0.1904   0.7679  0.4426  -0.2270   0.5193      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 21; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0456 (SE = 0.0241)
## tau (square root of estimated tau^2 value):             0.2136
## I^2 (residual heterogeneity / unaccounted variability): 70.06%
## H^2 (unaccounted variability / sampling variability):   3.34
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 59.7397, p-val < .0001
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 19.0866, p-val = 0.0008
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb    ci.ub      
## depthsSurface      -0.3280  0.0843  -3.8912  <.0001  -0.4932  -0.1628  *** 
## depthsSubsurface   -0.1681  0.1136  -1.4792  0.1391  -0.3908   0.0546      
## depthsDeep         -0.1281  0.1622  -0.7895  0.4298  -0.4459   0.1898      
## depthsVery deep    -0.1818  0.1707  -1.0649  0.2869  -0.5164   0.1528      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.1 Time since trawling

-> No significant regressions with time since trawling.

## 
## Multivariate Meta-Analysis Model (k = 34; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0767  0.2770      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 273.1451, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 55.7399, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.4620  0.1173  -3.9401  <.0001  -0.6919  -0.2322  *** 
## depthsSubsurface         0.4162  0.0995   4.1834  <.0001   0.2212   0.6112  *** 
## depthsDeep               0.3698  0.1007   3.6724  0.0002   0.1724   0.5672  *** 
## name                    -0.0390  0.0218  -1.7877  0.0738  -0.0817   0.0038    . 
## depthsSubsurface:name   -0.0329  0.0292  -1.1285  0.2591  -0.0901   0.0243      
## depthsDeep:name          0.0238  0.0293   0.8122  0.4167  -0.0336   0.0813      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 34; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0767  0.2770      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 273.1451, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 55.7399, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## intrcpt                                    -0.4620  0.1173  -3.9401  <.0001 
## depthsSubsurface                            0.4162  0.0995   4.1834  <.0001 
## depthsDeep                                  0.3698  0.1007   3.6724  0.0002 
## log(timesincetrawl + 1)                    -0.0390  0.0218  -1.7877  0.0738 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0329  0.0292  -1.1285  0.2591 
## depthsDeep:log(timesincetrawl + 1)          0.0238  0.0293   0.8122  0.4167 
##                                             ci.lb    ci.ub      
## intrcpt                                   -0.6919  -0.2322  *** 
## depthsSubsurface                           0.2212   0.6112  *** 
## depthsDeep                                 0.1724   0.5672  *** 
## log(timesincetrawl + 1)                   -0.0817   0.0038    . 
## depthsSubsurface:log(timesincetrawl + 1)  -0.0901   0.0243      
## depthsDeep:log(timesincetrawl + 1)        -0.0336   0.0813      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 34; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0767  0.2770      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 28) = 273.1451, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 66.0040, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## depthsSurface                              -0.4620  0.1173  -3.9401  <.0001 
## depthsSubsurface                           -0.0458  0.1321  -0.3469  0.7286 
## depthsDeep                                 -0.0922  0.1338  -0.6892  0.4907 
## log(timesincetrawl + 1)                    -0.0390  0.0218  -1.7877  0.0738 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0329  0.0292  -1.1285  0.2591 
## depthsDeep:log(timesincetrawl + 1)          0.0238  0.0293   0.8122  0.4167 
##                                             ci.lb    ci.ub      
## depthsSurface                             -0.6919  -0.2322  *** 
## depthsSubsurface                          -0.3047   0.2131      
## depthsDeep                                -0.3545   0.1701      
## log(timesincetrawl + 1)                   -0.0817   0.0038    . 
## depthsSubsurface:log(timesincetrawl + 1)  -0.0901   0.0243      
## depthsDeep:log(timesincetrawl + 1)        -0.0336   0.0813      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.2 Trawling frequency

-> 1 significant regression in deep strata.

## 
## Multivariate Meta-Analysis Model (k = 32; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1394  0.3734      6     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 26) = 262.5517, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 57.8538, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub      
## intrcpt                 -0.2827  0.2034  -1.3898  0.1646  -0.6814  0.1160      
## depthsSubsurface         0.1141  0.1422   0.8026  0.4222  -0.1645  0.3927      
## depthsDeep              -0.0391  0.1381  -0.2830  0.7772  -0.3099  0.2317      
## name                    -0.0171  0.0192  -0.8875  0.3748  -0.0547  0.0206      
## depthsSubsurface:name    0.0119  0.0073   1.6288  0.1033  -0.0024  0.0262      
## depthsDeep:name          0.0278  0.0071   3.9112  <.0001   0.0138  0.0417  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 32; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1394  0.3734      6     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 26) = 262.5517, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 57.8538, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                       -0.2827  0.2034  -1.3898  0.1646  -0.6814  0.1160 
## depthsSubsurface               0.1141  0.1422   0.8026  0.4222  -0.1645  0.3927 
## depthsDeep                    -0.0391  0.1381  -0.2830  0.7772  -0.3099  0.2317 
## heffortnum                    -0.0171  0.0192  -0.8875  0.3748  -0.0547  0.0206 
## depthsSubsurface:heffortnum    0.0119  0.0073   1.6288  0.1033  -0.0024  0.0262 
## depthsDeep:heffortnum          0.0278  0.0071   3.9112  <.0001   0.0138  0.0417 
##                                  
## intrcpt                          
## depthsSubsurface                 
## depthsDeep                       
## heffortnum                       
## depthsSubsurface:heffortnum      
## depthsDeep:heffortnum        *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 32; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1394  0.3734      6     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 26) = 262.5517, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 61.8587, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## depthsSurface                 -0.2827  0.2034  -1.3898  0.1646  -0.6814  0.1160 
## depthsSubsurface              -0.1686  0.2202  -0.7656  0.4439  -0.6002  0.2630 
## depthsDeep                    -0.3218  0.2196  -1.4656  0.1428  -0.7521  0.1086 
## heffortnum                    -0.0171  0.0192  -0.8875  0.3748  -0.0547  0.0206 
## depthsSubsurface:heffortnum    0.0119  0.0073   1.6288  0.1033  -0.0024  0.0262 
## depthsDeep:heffortnum          0.0278  0.0071   3.9112  <.0001   0.0138  0.0417 
##                                  
## depthsSurface                    
## depthsSubsurface                 
## depthsDeep                       
## heffortnum                       
## depthsSubsurface:heffortnum      
## depthsDeep:heffortnum        *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.3 Current velocity

–> Singificant regression with current velocity for several depth strata, more negative lnrr values in low current velocity areas.

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1010  0.3178      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 346.6483, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 55.1222, p-val < .0001
## 
## Model Results:
## 
##                        estimate        se     zval    pval      ci.lb     ci.ub 
## intrcpt                 -0.5547    0.2441  -2.2721  0.0231    -1.0332   -0.0762 
## depthsSubsurface        -0.2469    0.2504  -0.9859  0.3242    -0.7376    0.2439 
## depthsDeep             -82.2266   24.9753  -3.2923  0.0010  -131.1772  -33.2759 
## name                     1.9665    2.5801   0.7622  0.4459    -3.0903    7.0233 
## depthsSubsurface:name    4.0676    2.0028   2.0309  0.0423     0.1421    7.9930 
## depthsDeep:name        597.9881  180.6875   3.3095  0.0009   243.8472  952.1291 
##                            
## intrcpt                  * 
## depthsSubsurface           
## depthsDeep             *** 
## name                       
## depthsSubsurface:name    * 
## depthsDeep:name        *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1010  0.3178      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 346.6483, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 55.1222, p-val < .0001
## 
## Model Results:
## 
##                        estimate        se     zval    pval      ci.lb     ci.ub 
## intrcpt                 -0.5547    0.2441  -2.2721  0.0231    -1.0332   -0.0762 
## depthsSubsurface        -0.2469    0.2504  -0.9859  0.3242    -0.7376    0.2439 
## depthsDeep             -82.2266   24.9753  -3.2923  0.0010  -131.1772  -33.2759 
## cvel                     1.9665    2.5801   0.7622  0.4459    -3.0903    7.0233 
## depthsSubsurface:cvel    4.0676    2.0028   2.0309  0.0423     0.1421    7.9930 
## depthsDeep:cvel        597.9881  180.6875   3.3095  0.0009   243.8472  952.1291 
##                            
## intrcpt                  * 
## depthsSubsurface           
## depthsDeep             *** 
## cvel                       
## depthsSubsurface:cvel    * 
## depthsDeep:cvel        *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1010  0.3178      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 346.6483, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 62.9950, p-val < .0001
## 
## Model Results:
## 
##                        estimate        se     zval    pval      ci.lb     ci.ub 
## depthsSurface           -0.5547    0.2441  -2.2721  0.0231    -1.0332   -0.0762 
## depthsSubsurface        -0.8015    0.3291  -2.4352  0.0149    -1.4467   -0.1564 
## depthsDeep             -82.7813   24.9867  -3.3130  0.0009  -131.7542  -33.8083 
## cvel                     1.9665    2.5801   0.7622  0.4459    -3.0903    7.0233 
## depthsSubsurface:cvel    4.0676    2.0028   2.0309  0.0423     0.1421    7.9930 
## depthsDeep:cvel        597.9882  180.6875   3.3095  0.0009   243.8472  952.1291 
##                            
## depthsSurface            * 
## depthsSubsurface         * 
## depthsDeep             *** 
## cvel                       
## depthsSubsurface:cvel    * 
## depthsDeep:cvel        *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.4 Water depth

–> Singificant regression with water depth for several depth strata, more negative lnrr values in shallower water.

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0760  0.2758     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 43) = 349.5102, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 52.2283, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.4752  0.1210  -3.9279  <.0001  -0.7123  -0.2381  *** 
## depthsSubsurface         0.3206  0.0781   4.1030  <.0001   0.1674   0.4737  *** 
## depthsDeep              -0.0520  0.1540  -0.3377  0.7356  -0.3539   0.2499      
## name                     0.0018  0.0012   1.4684  0.1420  -0.0006   0.0043      
## depthsSubsurface:name   -0.0018  0.0006  -2.9413  0.0033  -0.0030  -0.0006   ** 
## depthsDeep:name          0.0113  0.0035   3.2537  0.0011   0.0045   0.0181   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0760  0.2758     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 43) = 349.5102, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 52.2283, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                     -0.4752  0.1210  -3.9279  <.0001  -0.7123  -0.2381 
## depthsSubsurface             0.3206  0.0781   4.1030  <.0001   0.1674   0.4737 
## depthsDeep                  -0.0520  0.1540  -0.3377  0.7356  -0.3539   0.2499 
## watdepth                     0.0018  0.0012   1.4684  0.1420  -0.0006   0.0043 
## depthsSubsurface:watdepth   -0.0018  0.0006  -2.9413  0.0033  -0.0030  -0.0006 
## depthsDeep:watdepth          0.0113  0.0035   3.2537  0.0011   0.0045   0.0181 
##                                
## intrcpt                    *** 
## depthsSubsurface           *** 
## depthsDeep                     
## watdepth                       
## depthsSubsurface:watdepth   ** 
## depthsDeep:watdepth         ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0760  0.2758     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 43) = 349.5102, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 61.8123, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## depthsSurface               -0.4752  0.1210  -3.9279  <.0001  -0.7123  -0.2381 
## depthsSubsurface            -0.1546  0.1290  -1.1988  0.2306  -0.4074   0.0982 
## depthsDeep                  -0.5272  0.1794  -2.9381  0.0033  -0.8789  -0.1755 
## watdepth                     0.0018  0.0012   1.4684  0.1420  -0.0006   0.0043 
## depthsSubsurface:watdepth   -0.0018  0.0006  -2.9413  0.0033  -0.0030  -0.0006 
## depthsDeep:watdepth          0.0113  0.0035   3.2537  0.0011   0.0045   0.0181 
##                                
## depthsSurface              *** 
## depthsSubsurface               
## depthsDeep                  ** 
## watdepth                       
## depthsSubsurface:watdepth   ** 
## depthsDeep:watdepth         ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.5 Primary productivity

-> significant regressions with surface primary production, but not always in the same direction. -> to me, the subsurface pos. regression is the odd one out.

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0454  0.2131      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 270.6403, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 59.7989, p-val < .0001
## 
## Model Results:
## 
##                        estimate        se     zval    pval     ci.lb     ci.ub 
## intrcpt                 -0.2243    0.1001  -2.2405  0.0251   -0.4205   -0.0281 
## depthsSubsurface        -0.0833    0.1507  -0.5527  0.5805   -0.3786    0.2120 
## depthsDeep              -9.6312    3.7065  -2.5985  0.0094  -16.8958   -2.3667 
## name                    -7.9924    2.7377  -2.9194  0.0035  -13.3581   -2.6266 
## depthsSubsurface:name   22.2003    8.4972   2.6127  0.0090    5.5461   38.8545 
## depthsDeep:name        504.8730  186.0709   2.7133  0.0067  140.1807  869.5653 
##                           
## intrcpt                 * 
## depthsSubsurface          
## depthsDeep             ** 
## name                   ** 
## depthsSubsurface:name  ** 
## depthsDeep:name        ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0454  0.2131      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 270.6403, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 59.7989, p-val < .0001
## 
## Model Results:
## 
##                           estimate        se     zval    pval     ci.lb 
## intrcpt                    -0.2243    0.1001  -2.2405  0.0251   -0.4205 
## depthsSubsurface           -0.0833    0.1507  -0.5527  0.5805   -0.3786 
## depthsDeep                 -9.6312    3.7065  -2.5985  0.0094  -16.8958 
## nppsurf                    -7.9924    2.7377  -2.9194  0.0035  -13.3581 
## depthsSubsurface:nppsurf   22.2003    8.4972   2.6127  0.0090    5.5461 
## depthsDeep:nppsurf        504.8730  186.0709   2.7133  0.0067  140.1807 
##                              ci.ub     
## intrcpt                    -0.0281   * 
## depthsSubsurface            0.2120     
## depthsDeep                 -2.3667  ** 
## nppsurf                    -2.6266  ** 
## depthsSubsurface:nppsurf   38.8545  ** 
## depthsDeep:nppsurf        869.5653  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 41; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0454  0.2131      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 35) = 270.6403, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 75.0951, p-val < .0001
## 
## Model Results:
## 
##                           estimate        se     zval    pval     ci.lb 
## depthsSurface              -0.2243    0.1001  -2.2405  0.0251   -0.4205 
## depthsSubsurface           -0.3076    0.1656  -1.8574  0.0633   -0.6321 
## depthsDeep                 -9.8555    3.7040  -2.6607  0.0078  -17.1153 
## nppsurf                    -7.9924    2.7377  -2.9194  0.0035  -13.3581 
## depthsSubsurface:nppsurf   22.2003    8.4972   2.6127  0.0090    5.5461 
## depthsDeep:nppsurf        504.8730  186.0709   2.7133  0.0067  140.1807 
##                              ci.ub     
## depthsSurface              -0.0281   * 
## depthsSubsurface            0.0170   . 
## depthsDeep                 -2.5957  ** 
## nppsurf                    -2.6266  ** 
## depthsSubsurface:nppsurf   38.8545  ** 
## depthsDeep:nppsurf        869.5653  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.6 Season

–> Not all levels present.

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0716  0.2676     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 39) = 330.0279, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 45.4649, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                          -0.3003  0.1121  -2.6794  0.0074  -0.5200 
## depthsSubsurface                 -0.1500  0.1278  -1.1738  0.2405  -0.4004 
## depthsDeep                        0.4268  0.0900   4.7395  <.0001   0.2503 
## hseasonSpring                     0.0055  0.1012   0.0548  0.9563  -0.1928 
## hseasonSummer                    -0.1241  0.0845  -1.4678  0.1422  -0.2898 
## hseasonWinter                    -0.0292  0.1204  -0.2425  0.8084  -0.2651 
## depthsSubsurface:hseasonSpring    0.3597  0.1522   2.3630  0.0181   0.0614 
## depthsDeep:hseasonSpring         -0.1569  0.1249  -1.2557  0.2092  -0.4017 
## depthsSubsurface:hseasonSummer    0.3009  0.1403   2.1437  0.0321   0.0258 
## depthsSubsurface:hseasonWinter    0.4588  0.1976   2.3220  0.0202   0.0715 
##                                   ci.ub      
## intrcpt                         -0.0806   ** 
## depthsSubsurface                 0.1005      
## depthsDeep                       0.6033  *** 
## hseasonSpring                    0.2039      
## hseasonSummer                    0.0416      
## hseasonWinter                    0.2067      
## depthsSubsurface:hseasonSpring   0.6581    * 
## depthsDeep:hseasonSpring         0.0880      
## depthsSubsurface:hseasonSummer   0.5759    * 
## depthsSubsurface:hseasonWinter   0.8461    * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0716  0.2676     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 39) = 330.0279, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 55.5458, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                    -0.3003  0.1121  -2.6794  0.0074  -0.5200 
## depthsSubsurface                 -0.4503  0.1404  -3.2067  0.0013  -0.7255 
## depthsDeep                        0.1265  0.1411   0.8965  0.3700  -0.1500 
## hseasonSpring                     0.0055  0.1012   0.0548  0.9563  -0.1928 
## hseasonSummer                    -0.1241  0.0845  -1.4678  0.1422  -0.2898 
## hseasonWinter                    -0.0292  0.1204  -0.2425  0.8084  -0.2651 
## depthsSubsurface:hseasonSpring    0.3597  0.1522   2.3630  0.0181   0.0614 
## depthsDeep:hseasonSpring         -0.1569  0.1249  -1.2557  0.2092  -0.4017 
## depthsSubsurface:hseasonSummer    0.3009  0.1403   2.1437  0.0321   0.0258 
## depthsSubsurface:hseasonWinter    0.4588  0.1976   2.3220  0.0202   0.0715 
##                                   ci.ub     
## depthsSurface                   -0.0806  ** 
## depthsSubsurface                -0.1751  ** 
## depthsDeep                       0.4030     
## hseasonSpring                    0.2039     
## hseasonSummer                    0.0416     
## hseasonWinter                    0.2067     
## depthsSubsurface:hseasonSpring   0.6581   * 
## depthsDeep:hseasonSpring         0.0880     
## depthsSubsurface:hseasonSummer   0.5759   * 
## depthsSubsurface:hseasonWinter   0.8461   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.5.7 Habitat

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0984  0.3137     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 43) = 341.8361, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 45.8584, p-val < .0001
## 
## Model Results:
## 
##                                        estimate      se     zval    pval 
## intrcpt                                 -0.3126  0.1350  -2.3147  0.0206 
## depthsSubsurface                        -0.0352  0.0702  -0.5014  0.6161 
## depthsDeep                               0.0170  0.1392   0.1222  0.9027 
## factor(hhabtype)Sand                    -0.1699  0.2141  -0.7936  0.4274 
## depthsSubsurface:factor(hhabtype)Sand    0.3703  0.0973   3.8066  0.0001 
## depthsDeep:factor(hhabtype)Sand          0.4673  0.1581   2.9557  0.0031 
##                                          ci.lb    ci.ub      
## intrcpt                                -0.5773  -0.0479    * 
## depthsSubsurface                       -0.1728   0.1024      
## depthsDeep                             -0.2559   0.2899      
## factor(hhabtype)Sand                   -0.5894   0.2497      
## depthsSubsurface:factor(hhabtype)Sand   0.1797   0.5610  *** 
## depthsDeep:factor(hhabtype)Sand         0.1574   0.7773   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 49; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0984  0.3137     10     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 43) = 341.8361, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 53.5538, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se     zval    pval    ci.lb 
## depthsSurface                   -0.3126  0.1350  -2.3147  0.0206  -0.5773 
## depthsSubsurface                -0.3478  0.1470  -2.3653  0.0180  -0.6360 
## depthsDeep                      -0.2956  0.1862  -1.5874  0.1124  -0.6605 
## hhabtypeSand                    -0.1699  0.2141  -0.7936  0.4274  -0.5894 
## depthsSubsurface:hhabtypeSand    0.3703  0.0973   3.8066  0.0001   0.1797 
## depthsDeep:hhabtypeSand          0.4673  0.1581   2.9557  0.0031   0.1574 
##                                  ci.ub      
## depthsSurface                  -0.0479    * 
## depthsSubsurface               -0.0596    * 
## depthsDeep                      0.0694      
## hhabtypeSand                    0.2497      
## depthsSubsurface:hhabtypeSand   0.5610  *** 
## depthsDeep:hhabtypeSand         0.7773   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6 Phytopigments

-> Slice depths not a significant predictor for phytopigment lnrr.

## 
## Mixed-Effects Model (k = 18; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.1518 (SE = 0.0663)
## tau (square root of estimated tau^2 value):             0.3897
## I^2 (residual heterogeneity / unaccounted variability): 88.26%
## H^2 (unaccounted variability / sampling variability):   8.52
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 14) = 93.0304, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 0.9150, p-val = 0.8218
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt            -0.0983  0.1473  -0.6674  0.5045  -0.3870  0.1904    
## depthsSubsurface   -0.1569  0.2260  -0.6945  0.4874  -0.5999  0.2860    
## depthsDeep         -0.0809  0.2861  -0.2826  0.7775  -0.6416  0.4799    
## depthsVery deep     0.2092  0.4376   0.4780  0.6326  -0.6485  1.0669    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 18; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.1518 (SE = 0.0663)
## tau (square root of estimated tau^2 value):             0.3897
## I^2 (residual heterogeneity / unaccounted variability): 88.26%
## H^2 (unaccounted variability / sampling variability):   8.52
## 
## Test for Residual Heterogeneity:
## QE(df = 14) = 93.0304, p-val < .0001
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 3.2695, p-val = 0.5138
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## depthsSurface      -0.0983  0.1473  -0.6674  0.5045  -0.3870  0.1904    
## depthsSubsurface   -0.2552  0.1714  -1.4893  0.1364  -0.5911  0.0807    
## depthsDeep         -0.1792  0.2453  -0.7305  0.4651  -0.6599  0.3016    
## depthsVery deep     0.1109  0.4121   0.2691  0.7878  -0.6968  0.9186    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.1 Time since trawling

-> Only significant relation with subsurface samples.

## 
## Multivariate Meta-Analysis Model (k = 19; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3376  0.5810      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 15) = 192.4876, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 18.7810, p-val = 0.0003
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub    
## intrcpt                 -0.1657  0.2668  -0.6209  0.5346  -0.6886   0.3573    
## depthsSubsurface        -0.2047  0.0853  -2.4010  0.0163  -0.3718  -0.0376  * 
## name                     0.0278  0.0312   0.8911  0.3729  -0.0333   0.0890    
## depthsSubsurface:name    0.1013  0.0412   2.4553  0.0141   0.0204   0.1821  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 25; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3374  0.5809      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 196.1680, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 45.9419, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## intrcpt                                    -0.1687  0.2667  -0.6326  0.5270 
## depthsSubsurface                           -0.2082  0.0852  -2.4452  0.0145 
## depthsDeep                                  0.3165  0.0930   3.4027  0.0007 
## log(timesincetrawl + 1)                     0.0318  0.0308   1.0315  0.3023 
## depthsSubsurface:log(timesincetrawl + 1)    0.1021  0.0412   2.4772  0.0132 
## depthsDeep:log(timesincetrawl + 1)         -0.0266  0.0468  -0.5677  0.5702 
##                                             ci.lb    ci.ub      
## intrcpt                                   -0.6915   0.3540      
## depthsSubsurface                          -0.3751  -0.0413    * 
## depthsDeep                                 0.1342   0.4988  *** 
## log(timesincetrawl + 1)                   -0.0286   0.0922      
## depthsSubsurface:log(timesincetrawl + 1)   0.0213   0.1829    * 
## depthsDeep:log(timesincetrawl + 1)        -0.1183   0.0652      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 25; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3374  0.5809      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 196.1680, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 46.1135, p-val < .0001
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## depthsSurface                              -0.1687  0.2667  -0.6326  0.5270 
## depthsSubsurface                           -0.3770  0.2716  -1.3878  0.1652 
## depthsDeep                                  0.1478  0.2740   0.5394  0.5896 
## log(timesincetrawl + 1)                     0.0318  0.0308   1.0315  0.3023 
## depthsSubsurface:log(timesincetrawl + 1)    0.1021  0.0412   2.4772  0.0132 
## depthsDeep:log(timesincetrawl + 1)         -0.0266  0.0468  -0.5677  0.5702 
##                                             ci.lb   ci.ub    
## depthsSurface                             -0.6915  0.3540    
## depthsSubsurface                          -0.9093  0.1554    
## depthsDeep                                -0.3892  0.6847    
## log(timesincetrawl + 1)                   -0.0286  0.0922    
## depthsSubsurface:log(timesincetrawl + 1)   0.0213  0.1829  * 
## depthsDeep:log(timesincetrawl + 1)        -0.1183  0.0652    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.2 Trawling frequency

-> Strong negative correlation between increasing trawling frequency and lnRR for for subsurface and full core samples.

## 
## Multivariate Meta-Analysis Model (k = 29; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1708  0.4132      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 23) = 208.5509, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 38.9941, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.3580  0.2324  -1.5407  0.1234  -0.8135   0.0974      
## depthsSubsurface        -0.0474  0.0786  -0.6039  0.5459  -0.2014   0.1065      
## depthsDeep               0.3605  0.0840   4.2907  <.0001   0.1959   0.5252  *** 
## name                     0.0004  0.0052   0.0790  0.9370  -0.0097   0.0105      
## depthsSubsurface:name   -0.0045  0.0020  -2.2521  0.0243  -0.0085  -0.0006    * 
## depthsDeep:name         -0.0081  0.0024  -3.3255  0.0009  -0.0128  -0.0033  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 29; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1708  0.4132      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 23) = 208.5509, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 38.9941, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb 
## intrcpt                       -0.3580  0.2324  -1.5407  0.1234  -0.8135 
## depthsSubsurface              -0.0474  0.0786  -0.6039  0.5459  -0.2014 
## depthsDeep                     0.3605  0.0840   4.2907  <.0001   0.1959 
## heffortnum                     0.0004  0.0052   0.0790  0.9370  -0.0097 
## depthsSubsurface:heffortnum   -0.0045  0.0020  -2.2521  0.0243  -0.0085 
## depthsDeep:heffortnum         -0.0081  0.0024  -3.3255  0.0009  -0.0128 
##                                ci.ub      
## intrcpt                       0.0974      
## depthsSubsurface              0.1065      
## depthsDeep                    0.5252  *** 
## heffortnum                    0.0105      
## depthsSubsurface:heffortnum  -0.0006    * 
## depthsDeep:heffortnum        -0.0033  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 29; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1708  0.4132      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 23) = 208.5509, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 43.3834, p-val < .0001
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb 
## depthsSurface                 -0.3580  0.2324  -1.5407  0.1234  -0.8135 
## depthsSubsurface              -0.4055  0.2364  -1.7152  0.0863  -0.8688 
## depthsDeep                     0.0025  0.2381   0.0106  0.9916  -0.4642 
## heffortnum                     0.0004  0.0052   0.0790  0.9370  -0.0097 
## depthsSubsurface:heffortnum   -0.0045  0.0020  -2.2521  0.0243  -0.0085 
## depthsDeep:heffortnum         -0.0081  0.0024  -3.3255  0.0009  -0.0128 
##                                ci.ub      
## depthsSurface                 0.0974      
## depthsSubsurface              0.0579    . 
## depthsDeep                    0.4693      
## heffortnum                    0.0105      
## depthsSubsurface:heffortnum  -0.0006    * 
## depthsDeep:heffortnum        -0.0033  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.3 Current velocity

-> Significant positive regressions for subsurface and deep strata, more negative lnRR with lower current velocity.

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3205  0.5661      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 254.6658, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 39.9055, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.0929  0.4007  -0.2317  0.8168  -0.8783   0.6925      
## depthsSubsurface        -0.5865  0.1661  -3.5308  0.0004  -0.9121  -0.2609  *** 
## depthsDeep              -0.6306  0.2950  -2.1373  0.0326  -1.2088  -0.0523    * 
## name                    -0.5293  4.7102  -0.1124  0.9105  -9.7611   8.7025      
## depthsSubsurface:name    4.1223  1.5136   2.7236  0.0065   1.1558   7.0889   ** 
## depthsDeep:name          6.8588  2.2913   2.9934  0.0028   2.3679  11.3497   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3205  0.5661      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 254.6658, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 39.9055, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.0929  0.4007  -0.2317  0.8168  -0.8783   0.6925      
## depthsSubsurface        -0.5865  0.1661  -3.5308  0.0004  -0.9121  -0.2609  *** 
## depthsDeep              -0.6306  0.2950  -2.1373  0.0326  -1.2088  -0.0523    * 
## cvel                    -0.5293  4.7102  -0.1124  0.9105  -9.7611   8.7025      
## depthsSubsurface:cvel    4.1223  1.5136   2.7236  0.0065   1.1558   7.0889   ** 
## depthsDeep:cvel          6.8588  2.2913   2.9934  0.0028   2.3679  11.3497   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3205  0.5661      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 254.6658, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 40.6156, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub     
## depthsSurface           -0.0929  0.4007  -0.2317  0.8168  -0.8783   0.6925     
## depthsSubsurface        -0.6794  0.4194  -1.6199  0.1053  -1.5014   0.1426     
## depthsDeep              -0.7234  0.4836  -1.4958  0.1347  -1.6713   0.2245     
## cvel                    -0.5293  4.7102  -0.1124  0.9105  -9.7611   8.7025     
## depthsSubsurface:cvel    4.1223  1.5136   2.7236  0.0065   1.1558   7.0889  ** 
## depthsDeep:cvel          6.8588  2.2913   2.9934  0.0028   2.3679  11.3497  ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.4 Water depth

-> Significant positive regressions for subsurface and deep strata, more negative lnRR with shallower water depth.

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2309  0.4805      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 278.6294, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 39.3251, p-val < .0001
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub      
## intrcpt                 -0.1089  0.2081  -0.5233  0.6008  -0.5168   0.2990      
## depthsSubsurface        -0.0101  0.0632  -0.1592  0.8735  -0.1338   0.1137      
## depthsDeep               0.3439  0.0764   4.5043  <.0001   0.1943   0.4936  *** 
## name                    -0.0003  0.0010  -0.3368  0.7363  -0.0023   0.0016      
## depthsSubsurface:name   -0.0008  0.0003  -2.5299  0.0114  -0.0015  -0.0002    * 
## depthsDeep:name         -0.0014  0.0004  -3.1637  0.0016  -0.0022  -0.0005   ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2309  0.4805      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 278.6294, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 39.3251, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## intrcpt                     -0.1089  0.2081  -0.5233  0.6008  -0.5168   0.2990 
## depthsSubsurface            -0.0101  0.0632  -0.1592  0.8735  -0.1338   0.1137 
## depthsDeep                   0.3439  0.0764   4.5043  <.0001   0.1943   0.4936 
## watdepth                    -0.0003  0.0010  -0.3368  0.7363  -0.0023   0.0016 
## depthsSubsurface:watdepth   -0.0008  0.0003  -2.5299  0.0114  -0.0015  -0.0002 
## depthsDeep:watdepth         -0.0014  0.0004  -3.1637  0.0016  -0.0022  -0.0005 
##                                
## intrcpt                        
## depthsSubsurface               
## depthsDeep                 *** 
## watdepth                       
## depthsSubsurface:watdepth    * 
## depthsDeep:watdepth         ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2309  0.4805      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 278.6294, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 40.3540, p-val < .0001
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb    ci.ub 
## depthsSurface               -0.1089  0.2081  -0.5233  0.6008  -0.5168   0.2990 
## depthsSubsurface            -0.1190  0.2106  -0.5649  0.5721  -0.5317   0.2938 
## depthsDeep                   0.2350  0.2153   1.0916  0.2750  -0.1870   0.6570 
## watdepth                    -0.0003  0.0010  -0.3368  0.7363  -0.0023   0.0016 
## depthsSubsurface:watdepth   -0.0008  0.0003  -2.5299  0.0114  -0.0015  -0.0002 
## depthsDeep:watdepth         -0.0014  0.0004  -3.1637  0.0016  -0.0022  -0.0005 
##                               
## depthsSurface                 
## depthsSubsurface              
## depthsDeep                    
## watdepth                      
## depthsSubsurface:watdepth   * 
## depthsDeep:watdepth        ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.5 Primary productivity

-> Significant regressions, but no consistent sign.

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1557  0.3946      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 176.5734, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 43.0625, p-val < .0001
## 
## Model Results:
## 
##                        estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                  0.0887   0.1915   0.4633  0.6432   -0.2866   0.4640 
## depthsSubsurface        -0.3759   0.0999  -3.7629  0.0002   -0.5716  -0.1801 
## depthsDeep              -0.3475   0.2057  -1.6892  0.0912   -0.7508   0.0557 
## name                   -10.3398   4.8522  -2.1310  0.0331  -19.8499  -0.8297 
## depthsSubsurface:name   17.0669   6.7040   2.5458  0.0109    3.9273  30.2065 
## depthsDeep:name         32.6288  11.0635   2.9492  0.0032   10.9448  54.3129 
##                            
## intrcpt                    
## depthsSubsurface       *** 
## depthsDeep               . 
## name                     * 
## depthsSubsurface:name    * 
## depthsDeep:name         ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1557  0.3946      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 176.5734, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 43.0625, p-val < .0001
## 
## Model Results:
## 
##                           estimate       se     zval    pval     ci.lb    ci.ub 
## intrcpt                     0.0887   0.1915   0.4633  0.6432   -0.2866   0.4640 
## depthsSubsurface           -0.3759   0.0999  -3.7629  0.0002   -0.5716  -0.1801 
## depthsDeep                 -0.3475   0.2057  -1.6892  0.0912   -0.7508   0.0557 
## nppsurf                   -10.3398   4.8522  -2.1310  0.0331  -19.8499  -0.8297 
## depthsSubsurface:nppsurf   17.0669   6.7040   2.5458  0.0109    3.9273  30.2065 
## depthsDeep:nppsurf         32.6288  11.0635   2.9492  0.0032   10.9448  54.3129 
##                               
## intrcpt                       
## depthsSubsurface          *** 
## depthsDeep                  . 
## nppsurf                     * 
## depthsSubsurface:nppsurf    * 
## depthsDeep:nppsurf         ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 35; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.1557  0.3946      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 29) = 176.5734, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 44.6928, p-val < .0001
## 
## Model Results:
## 
##                           estimate       se     zval    pval     ci.lb    ci.ub 
## depthsSurface               0.0887   0.1915   0.4633  0.6432   -0.2866   0.4640 
## depthsSubsurface           -0.2872   0.2020  -1.4216  0.1552   -0.6831   0.1088 
## depthsDeep                 -0.2588   0.2688  -0.9627  0.3357   -0.7858   0.2681 
## nppsurf                   -10.3398   4.8522  -2.1310  0.0331  -19.8499  -0.8297 
## depthsSubsurface:nppsurf   17.0669   6.7040   2.5458  0.0109    3.9273  30.2065 
## depthsDeep:nppsurf         32.6288  11.0635   2.9492  0.0032   10.9448  54.3129 
##                              
## depthsSurface                
## depthsSubsurface             
## depthsDeep                   
## nppsurf                    * 
## depthsSubsurface:nppsurf   * 
## depthsDeep:nppsurf        ** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.6 Season

-> Only very weak correlations.

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3037  0.5511      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 33) = 298.8090, p-val < .0001
## 
## Test of Moderators (coefficients 2:10):
## QM(df = 9) = 47.5801, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## intrcpt                          -0.3239  0.2207  -1.4678  0.1421  -0.7564 
## depthsSubsurface                 -0.1077  0.1406  -0.7665  0.4434  -0.3832 
## depthsDeep                       -0.0040  0.1288  -0.0313  0.9750  -0.2564 
## hseasonSpring                     0.1095  0.1224   0.8951  0.3707  -0.1303 
## hseasonSummer                     0.2402  0.1250   1.9213  0.0547  -0.0048 
## hseasonWinter                     0.1547  0.1546   1.0002  0.3172  -0.1484 
## depthsSubsurface:hseasonSpring   -0.1017  0.1604  -0.6339  0.5262  -0.4161 
## depthsDeep:hseasonSpring          0.2508  0.1510   1.6609  0.0967  -0.0452 
## depthsSubsurface:hseasonSummer    0.0020  0.1569   0.0127  0.9898  -0.3055 
## depthsSubsurface:hseasonWinter    0.2775  0.2172   1.2777  0.2014  -0.1482 
##                                  ci.ub    
## intrcpt                         0.1086    
## depthsSubsurface                0.1677    
## depthsDeep                      0.2484    
## hseasonSpring                   0.3494    
## hseasonSummer                   0.4853  . 
## hseasonWinter                   0.4577    
## depthsSubsurface:hseasonSpring  0.2127    
## depthsDeep:hseasonSpring        0.5468  . 
## depthsSubsurface:hseasonSummer  0.3095    
## depthsSubsurface:hseasonWinter  0.7032    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.3037  0.5511      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 33) = 298.8090, p-val < .0001
## 
## Test of Moderators (coefficients 1:10):
## QM(df = 10) = 48.3353, p-val < .0001
## 
## Model Results:
## 
##                                 estimate      se     zval    pval    ci.lb 
## depthsSurface                    -0.3239  0.2207  -1.4678  0.1421  -0.7564 
## depthsSubsurface                 -0.4316  0.2263  -1.9074  0.0565  -0.8752 
## depthsDeep                       -0.3279  0.2558  -1.2820  0.1998  -0.8293 
## hseasonSpring                     0.1095  0.1224   0.8951  0.3707  -0.1303 
## hseasonSummer                     0.2402  0.1250   1.9213  0.0547  -0.0048 
## hseasonWinter                     0.1547  0.1546   1.0002  0.3172  -0.1484 
## depthsSubsurface:hseasonSpring   -0.1017  0.1604  -0.6339  0.5262  -0.4161 
## depthsDeep:hseasonSpring          0.2508  0.1510   1.6609  0.0967  -0.0452 
## depthsSubsurface:hseasonSummer    0.0020  0.1569   0.0127  0.9898  -0.3055 
## depthsSubsurface:hseasonWinter    0.2775  0.2172   1.2777  0.2014  -0.1482 
##                                  ci.ub    
## depthsSurface                   0.1086    
## depthsSubsurface                0.0119  . 
## depthsDeep                      0.1734    
## hseasonSpring                   0.3494    
## hseasonSummer                   0.4853  . 
## hseasonWinter                   0.4577    
## depthsSubsurface:hseasonSpring  0.2127    
## depthsDeep:hseasonSpring        0.5468  . 
## depthsSubsurface:hseasonSummer  0.3095    
## depthsSubsurface:hseasonWinter  0.7032    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.6.7 Habitat

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2518  0.5018      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 303.3824, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## QM(df = 5) = 32.4064, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se     zval    pval    ci.lb 
## intrcpt                         -0.2362  0.2589  -0.9123  0.3616  -0.7436 
## depthsSubsurface                -0.1625  0.0693  -2.3459  0.0190  -0.2982 
## depthsDeep                      -0.1875  0.1951  -0.9610  0.3365  -0.5698 
## hhabtypeSand                     0.1529  0.3634   0.4206  0.6741  -0.5595 
## depthsSubsurface:hhabtypeSand    0.0785  0.0937   0.8376  0.4023  -0.1052 
## depthsDeep:hhabtypeSand          0.4674  0.2084   2.2431  0.0249   0.0590 
##                                  ci.ub    
## intrcpt                         0.2712    
## depthsSubsurface               -0.0267  * 
## depthsDeep                      0.1949    
## hhabtypeSand                    0.8652    
## depthsSubsurface:hhabtypeSand   0.2621    
## depthsDeep:hhabtypeSand         0.8758  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 43; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.2518  0.5018      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 37) = 303.3824, p-val < .0001
## 
## Test of Moderators (coefficients 1:6):
## QM(df = 6) = 33.3386, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se     zval    pval    ci.lb 
## depthsSurface                   -0.2362  0.2589  -0.9123  0.3616  -0.7436 
## depthsSubsurface                -0.3987  0.2626  -1.5183  0.1289  -0.9133 
## depthsDeep                      -0.4236  0.3158  -1.3414  0.1798  -1.0426 
## hhabtypeSand                     0.1529  0.3634   0.4206  0.6741  -0.5595 
## depthsSubsurface:hhabtypeSand    0.0785  0.0937   0.8376  0.4023  -0.1052 
## depthsDeep:hhabtypeSand          0.4674  0.2084   2.2431  0.0249   0.0590 
##                                 ci.ub    
## depthsSurface                  0.2712    
## depthsSubsurface               0.1160    
## depthsDeep                     0.1953    
## hhabtypeSand                   0.8652    
## depthsSubsurface:hhabtypeSand  0.2621    
## depthsDeep:hhabtypeSand        0.8758  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7 Mean grain size

-> No significant predictor, but lnRR for surface and subsurface strata significantly below 0.

## 
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0067 (SE = 0.0097)
## tau (square root of estimated tau^2 value):             0.0819
## I^2 (residual heterogeneity / unaccounted variability): 32.31%
## H^2 (unaccounted variability / sampling variability):   1.48
## R^2 (amount of heterogeneity accounted for):            0.00%
## 
## Test for Residual Heterogeneity:
## QE(df = 9) = 15.8610, p-val = 0.0698
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.7997, p-val = 0.6704
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt             -0.1415  0.0730  -1.9379  0.0526  -0.2846  0.0016  . 
## depthsSubsurface     0.0311  0.0916   0.3400  0.7339  -0.1484  0.2107    
## depthsFull sample    0.1354  0.1514   0.8943  0.3712  -0.1613  0.4320    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Mixed-Effects Model (k = 12; tau^2 estimator: REML)
## 
## tau^2 (estimated amount of residual heterogeneity):     0.0067 (SE = 0.0097)
## tau (square root of estimated tau^2 value):             0.0819
## I^2 (residual heterogeneity / unaccounted variability): 32.31%
## H^2 (unaccounted variability / sampling variability):   1.48
## 
## Test for Residual Heterogeneity:
## QE(df = 9) = 15.8610, p-val = 0.0698
## 
## Test of Moderators (coefficients 1:3):
## QM(df = 3) = 7.7395, p-val = 0.0517
## 
## Model Results:
## 
##                    estimate      se     zval    pval    ci.lb    ci.ub    
## depthsSurface       -0.1415  0.0730  -1.9379  0.0526  -0.2846   0.0016  . 
## depthsSubsurface    -0.1104  0.0553  -1.9954  0.0460  -0.2187  -0.0020  * 
## depthsFull sample   -0.0061  0.1326  -0.0463  0.9631  -0.2660   0.2537    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.1 Time since trawling

-> No sig. relationship with time since trawling

## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 15.3016, p-val = 0.1691
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 4.0327, p-val = 0.2580
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                 -0.0063  0.0535  -0.1176  0.9064  -0.1112  0.0986    
## depthsSubsurface         0.0607  0.1186   0.5115  0.6090  -0.1718  0.2932    
## name                    -0.0037  0.0145  -0.2532  0.8001  -0.0320  0.0247    
## depthsSubsurface:name   -0.0257  0.0229  -1.1200  0.2627  -0.0706  0.0193    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 15.3016, p-val = 0.1691
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 4.0327, p-val = 0.2580
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## intrcpt                                    -0.0063  0.0535  -0.1176  0.9064 
## depthsSubsurface                            0.0607  0.1186   0.5115  0.6090 
## log(timesincetrawl + 1)                    -0.0037  0.0145  -0.2532  0.8001 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0257  0.0229  -1.1200  0.2627 
##                                             ci.lb   ci.ub    
## intrcpt                                   -0.1112  0.0986    
## depthsSubsurface                          -0.1718  0.2932    
## log(timesincetrawl + 1)                   -0.0320  0.0247    
## depthsSubsurface:log(timesincetrawl + 1)  -0.0706  0.0193    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 15; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      5     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 11) = 15.3016, p-val = 0.1691
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 5.5563, p-val = 0.2348
## 
## Model Results:
## 
##                                           estimate      se     zval    pval 
## depthsSurface                              -0.0063  0.0535  -0.1176  0.9064 
## depthsSubsurface                            0.0544  0.1059   0.5137  0.6075 
## log(timesincetrawl + 1)                    -0.0037  0.0145  -0.2532  0.8001 
## depthsSubsurface:log(timesincetrawl + 1)   -0.0257  0.0229  -1.1200  0.2627 
##                                             ci.lb   ci.ub    
## depthsSurface                             -0.1112  0.0986    
## depthsSubsurface                          -0.1531  0.2619    
## log(timesincetrawl + 1)                   -0.0320  0.0247    
## depthsSubsurface:log(timesincetrawl + 1)  -0.0706  0.0193    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.2 Trawling frequency

-> No sig. relationship with trawling frequency.

## 
## Multivariate Meta-Analysis Model (k = 17; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0069  0.0829      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 13) = 20.1330, p-val = 0.0919
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.1695, p-val = 0.7603
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                  0.0082  0.1235   0.0666  0.9469  -0.2339  0.2503    
## depthsSubsurface        -0.1557  0.1949  -0.7986  0.4245  -0.5378  0.2264    
## name                    -0.0047  0.0156  -0.2999  0.7643  -0.0353  0.0260    
## depthsSubsurface:name    0.0072  0.0181   0.4008  0.6886  -0.0282  0.0426    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 17; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0069  0.0829      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 13) = 20.1330, p-val = 0.0919
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 1.1695, p-val = 0.7603
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                        0.0082  0.1235   0.0666  0.9469  -0.2339  0.2503 
## depthsSubsurface              -0.1557  0.1949  -0.7986  0.4245  -0.5378  0.2264 
## heffortnum                    -0.0047  0.0156  -0.2999  0.7643  -0.0353  0.0260 
## depthsSubsurface:heffortnum    0.0072  0.0181   0.4008  0.6886  -0.0282  0.0426 
##                                
## intrcpt                        
## depthsSubsurface               
## heffortnum                     
## depthsSubsurface:heffortnum    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 17; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0069  0.0829      7     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 13) = 20.1330, p-val = 0.0919
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 3.4987, p-val = 0.4781
## 
## Model Results:
## 
##                              estimate      se     zval    pval    ci.lb   ci.ub 
## depthsSurface                  0.0082  0.1235   0.0666  0.9469  -0.2339  0.2503 
## depthsSubsurface              -0.1475  0.1488  -0.9912  0.3216  -0.4390  0.1441 
## heffortnum                    -0.0047  0.0156  -0.2999  0.7643  -0.0353  0.0260 
## depthsSubsurface:heffortnum    0.0072  0.0181   0.4008  0.6886  -0.0282  0.0426 
##                                
## depthsSurface                  
## depthsSubsurface               
## heffortnum                     
## depthsSubsurface:heffortnum    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.3 Current velocity

-> No significant relation with current velocity.

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.4138, p-val = 0.9990
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.8929, p-val = 0.1169
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                 -0.0645  0.0704  -0.9156  0.3599  -0.2025  0.0735    
## depthsSubsurface        -0.1622  0.1087  -1.4924  0.1356  -0.3751  0.0508    
## name                     0.5614  0.8205   0.6842  0.4939  -1.0467  2.1694    
## depthsSubsurface:name    1.7729  1.5469   1.1461  0.2517  -1.2589  4.8048    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.4138, p-val = 0.9990
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 5.8929, p-val = 0.1169
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                 -0.0645  0.0704  -0.9156  0.3599  -0.2025  0.0735    
## depthsSubsurface        -0.1622  0.1087  -1.4924  0.1356  -0.3751  0.0508    
## cvel                     0.5614  0.8205   0.6842  0.4939  -1.0467  2.1694    
## depthsSubsurface:cvel    1.7729  1.5469   1.1461  0.2517  -1.2589  4.8048    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.4138, p-val = 0.9990
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 11.0884, p-val = 0.0256
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb    ci.ub     
## depthsSurface           -0.0645  0.0704  -0.9156  0.3599  -0.2025   0.0735     
## depthsSubsurface        -0.2266  0.0828  -2.7387  0.0062  -0.3888  -0.0644  ** 
## cvel                     0.5614  0.8205   0.6842  0.4939  -1.0467   2.1694     
## depthsSubsurface:cvel    1.7729  1.5469   1.1461  0.2517  -1.2589   4.8048     
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.4 Water depth

-> No significant relation with water depth

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 8.6410, p-val = 0.9675
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.2572, p-val = 0.0997
## 
## Model Results:
## 
##                        estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt                 -0.3723  0.3022  -1.2318  0.2180  -0.9647  0.2201    
## depthsSubsurface         0.3705  0.3082   1.2023  0.2292  -0.2335  0.9745    
## name                     0.0108  0.0097   1.1182  0.2635  -0.0082  0.0298    
## depthsSubsurface:name   -0.0124  0.0097  -1.2795  0.2007  -0.0315  0.0066    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 8.6410, p-val = 0.9675
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.2572, p-val = 0.0997
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb   ci.ub 
## intrcpt                     -0.3723  0.3022  -1.2318  0.2180  -0.9647  0.2201 
## depthsSubsurface             0.3705  0.3082   1.2023  0.2292  -0.2335  0.9745 
## watdepth                     0.0108  0.0097   1.1182  0.2635  -0.0082  0.0298 
## depthsSubsurface:watdepth   -0.0124  0.0097  -1.2795  0.2007  -0.0315  0.0066 
##                              
## intrcpt                      
## depthsSubsurface             
## watdepth                     
## depthsSubsurface:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0000  0.0000      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 18) = 8.6410, p-val = 0.9675
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 13.1119, p-val = 0.0107
## 
## Model Results:
## 
##                            estimate      se     zval    pval    ci.lb   ci.ub 
## depthsSurface               -0.3723  0.3022  -1.2318  0.2180  -0.9647  0.2201 
## depthsSubsurface            -0.0018  0.0601  -0.0296  0.9764  -0.1196  0.1160 
## watdepth                     0.0108  0.0097   1.1182  0.2635  -0.0082  0.0298 
## depthsSubsurface:watdepth   -0.0124  0.0097  -1.2795  0.2007  -0.0315  0.0066 
##                              
## depthsSurface                
## depthsSubsurface             
## watdepth                     
## depthsSubsurface:watdepth    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.5 Primary productivity

-> No significant relation with primary productivity.

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0002  0.0127      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.1321, p-val = 0.9994
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.0805, p-val = 0.1078
## 
## Model Results:
## 
##                        estimate      se     zval    pval     ci.lb   ci.ub    
## intrcpt                 -0.0158  0.0603  -0.2620  0.7933   -0.1339  0.1023    
## depthsSubsurface         0.0162  0.0836   0.1938  0.8464   -0.1477  0.1801    
## name                    -0.5320  4.8810  -0.1090  0.9132  -10.0985  9.0345    
## depthsSubsurface:name   -8.3963  6.6135  -1.2696  0.2042  -21.3585  4.5660    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0002  0.0127      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.1321, p-val = 0.9994
## 
## Test of Moderators (coefficients 2:4):
## QM(df = 3) = 6.0805, p-val = 0.1078
## 
## Model Results:
## 
##                           estimate      se     zval    pval     ci.lb   ci.ub 
## intrcpt                    -0.0158  0.0603  -0.2620  0.7933   -0.1339  0.1023 
## depthsSubsurface            0.0162  0.0836   0.1938  0.8464   -0.1477  0.1801 
## nppsurf                    -0.5320  4.8810  -0.1090  0.9132  -10.0985  9.0345 
## depthsSubsurface:nppsurf   -8.3963  6.6135  -1.2696  0.2042  -21.3585  4.5660 
##                             
## intrcpt                     
## depthsSubsurface            
## nppsurf                     
## depthsSubsurface:nppsurf    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 21; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0002  0.0127      8     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 4.1321, p-val = 0.9994
## 
## Test of Moderators (coefficients 1:4):
## QM(df = 4) = 11.0985, p-val = 0.0255
## 
## Model Results:
## 
##                           estimate      se     zval    pval     ci.lb   ci.ub 
## depthsSurface              -0.0158  0.0603  -0.2620  0.7933   -0.1339  0.1023 
## depthsSubsurface            0.0004  0.0593   0.0070  0.9944   -0.1159  0.1167 
## nppsurf                    -0.5320  4.8810  -0.1090  0.9132  -10.0985  9.0345 
## depthsSubsurface:nppsurf   -8.3963  6.6135  -1.2696  0.2042  -21.3585  4.5660 
##                             
## depthsSurface               
## depthsSubsurface            
## nppsurf                     
## depthsSubsurface:nppsurf    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.6 Season

-> No significant relation with season.

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0059  0.0766      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 11.8584, p-val = 0.8086
## 
## Test of Moderators (coefficients 2:5):
## QM(df = 4) = 1.3863, p-val = 0.8466
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt            -0.0882  0.1004  -0.8788  0.3795  -0.2849  0.1085    
## depthsSubsurface   -0.0614  0.0662  -0.9288  0.3530  -0.1911  0.0682    
## hseasonSpring      -0.0324  0.0971  -0.3335  0.7388  -0.2226  0.1579    
## hseasonSummer       0.0477  0.1172   0.4068  0.6842  -0.1820  0.2774    
## hseasonYearly       0.1383  0.1741   0.7941  0.4271  -0.2030  0.4795    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0059  0.0766      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 17) = 11.8584, p-val = 0.8086
## 
## Test of Moderators (coefficients 1:5):
## QM(df = 5) = 5.8874, p-val = 0.3173
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## depthsSurface      -0.0882  0.1004  -0.8788  0.3795  -0.2849  0.1085    
## depthsSubsurface   -0.1496  0.1202  -1.2449  0.2132  -0.3852  0.0859    
## hseasonSpring      -0.0324  0.0971  -0.3335  0.7388  -0.2226  0.1579    
## hseasonSummer       0.0477  0.1172   0.4068  0.6842  -0.1820  0.2774    
## hseasonYearly       0.1383  0.1741   0.7941  0.4271  -0.2030  0.4795    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.7.7 Habitat

-> No significant relation with habitat type.

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0047  0.0688      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 12.8882, p-val = 0.8442
## 
## Test of Moderators (coefficients 2:3):
## QM(df = 2) = 0.9175, p-val = 0.6321
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## intrcpt            -0.1012  0.0766  -1.3208  0.1866  -0.2513  0.0490    
## depthsSubsurface   -0.0567  0.0638  -0.8876  0.3747  -0.1817  0.0684    
## hhabtypeSand        0.0682  0.0959   0.7114  0.4768  -0.1197  0.2561    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Multivariate Meta-Analysis Model (k = 22; method: REML)
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed           factor 
## sigma^2    0.0047  0.0688      9     no  article_type_id 
## 
## Test for Residual Heterogeneity:
## QE(df = 19) = 12.8882, p-val = 0.8442
## 
## Test of Moderators (coefficients 1:3):
## QM(df = 3) = 5.6645, p-val = 0.1291
## 
## Model Results:
## 
##                   estimate      se     zval    pval    ci.lb   ci.ub    
## depthsSurface      -0.1012  0.0766  -1.3208  0.1866  -0.2513  0.0490    
## depthsSubsurface   -0.1578  0.0997  -1.5829  0.1134  -0.3533  0.0376    
## hhabtypeSand        0.0682  0.0959   0.7114  0.4768  -0.1197  0.2561    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1